Patent Application
20250192914
The following relates to wireless communications, including model updates with user equipment latent query.
Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, and power). Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple-access communications system may include one or more base stations, each supporting wireless communication for communication devices, which may be known as user equipment (UE).
The described techniques relate to improved methods, systems, devices, and apparatuses that support model updates (e.g., updates of machine learning models, such as for use as part of one or more neural networks) with user equipment (UE) latent query. For example, the described techniques provide for two-sided network-first modeling to improve channel state monitoring and feedback reporting. For example, a UE may transmit or otherwise provide an indication of a first training dataset to a network entity. The first training dataset may be associated with a network-based auto-encoder (e.g., a nominal or reference encoder/decoder implemented at the network entity or at a network server associated with the network entity). In some aspects, the first training dataset may be based on measurements or other observations of the UE for the channel (e.g., channel metrics or precoding metrics) between the UE and the network entity. The first training dataset may be used as training inputs for the network-based auto-encoder. For example, the first training dataset may include the channel metrics, the precoding metric, or both.
The network entity may receive the first training dataset and input this information into a network-based encoder (e.g., the encoder portion of the network-based auto-encoder), the output of which includes a second training dataset associated with the network-based decoder. The second training dataset may be based on the training inputs for the network-based auto-encoder. For example, the second training dataset may be the output of the encoder portion of the network-based auto-encoder. The UE may receive the second training dataset and use this information as inputs to a UE-based encoder associated with or otherwise corresponding to the network-based decoder. This technique may permit the UE to train its encoder to the decoder of the network entity, but without knowing or otherwise being aware of the model that the network entity has deployed for its network-based auto-encoder.
A method for wireless communications by a UE is described. The method may include transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
A UE for wireless communications is described. The UE may include one or more memories storing processor executable code, and one or more processors coupled with the one or more memories. The one or more processors may individually or collectively be operable to execute the code to cause the UE to transmit an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and receive, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Another UE for wireless communications is described. The UE may include means for transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and means for receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors to transmit an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and receive, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for training the UE-based encoder using the second training dataset.
Some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for transmitting the second training dataset to a UE server associated with the UE and receiving an updated encoder model for the UE-based encoder from the UE server, where the updated encoder model may be based on the second training dataset.
Some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving information associated with the network-based auto-encoder, where transmitting the indication of the first training dataset may be based on the information.
In some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder includes an identifier associated with the network-based auto-encoder.
In some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE.
In some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active.
In some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein, the first training dataset may be transmitted as a non-compressed dataset.
Some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for applying a compression algorithm to the first training dataset prior to transmission.
In some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein, the compression algorithm includes at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm.
Some examples of the method, user equipment (UEs), and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving an indication that the second training dataset is for training the UE-based encoder of the UE.
A method for wireless communications by a network entity is described. The method may include obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
A network entity for wireless communications is described. The network entity may include one or more memories storing processor executable code, and one or more processors coupled with the one or more memories. The one or more processors may individually or collectively be operable to execute the code to cause the network entity to obtain, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and provide for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Another network entity for wireless communications is described. The network entity may include means for obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and means for providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors to obtain, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both, and provide for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Some examples of the method, network entities, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for inputting the first training dataset into a network-based encoder of the network-based auto-encoder, where an output of the network-based encoder provides the second training dataset.
Some examples of the method, network entities, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for providing the first training dataset for output to a network server associated with the network entity and obtaining the second training dataset from the network server in response to providing the first training dataset for output.
Some examples of the method, network entities, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for providing for output information associated with the network-based auto-encoder, where obtaining the indication of the first training dataset may be based on the information.
In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder includes an identifier associated with the network-based auto-encoder.
In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE.
some examples of the method, network entities, and non-transitory computer-readable medium described herein, the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active.
In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first training dataset may be obtained as a non-compressed dataset.
In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first training dataset may be obtained as a compressed dataset based on a compression algorithm.
In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the compression algorithm includes at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm.
Wireless networks may use modeling techniques (e.g., machine learning models or other models, which may include non-machine learning models) to improve channel state feedback reporting between a user equipment (UE) and the network. The models may include a nominal or reference encoder/decoder pair that are trained using channel performance metrics of the channel between the UE and the network entity. Different types of modeling techniques may include the encoder being implemented on the UE and the decoder being implemented on the network entity, the encoder/decoder being implemented on the UE (e.g., a UE-based auto-encoder), or the encoder/decoder implemented on the network entity (e.g., a network-based auto-encoder). However, some networks employ models that are otherwise unknown (e.g., proprietary) by other devices operating on the network. These modeling techniques may not provide a mechanism for two-sided modeling using a network-first approach that involves the UE.
Accordingly, the described techniques provide for two-sided network-first modeling to improve channel state monitoring and feedback reporting. For example, a UE may transmit or otherwise provide an indication of a first training dataset to a network entity. The first training dataset may be associated with a network-based auto-encoder (e.g., a nominal or reference encoder/decoder implemented at the network entity or at a network server associated with the network entity). In some aspects, the first training dataset may be based on measurements or other observations of the UE for the channel (e.g., channel metrics) between the UE and the network entity. The first training dataset may be used as training inputs for the network-based auto-encoder. For example, the first training dataset may include the channel metrics or include a precoding metric.
The network entity may receive the first training dataset and input this information into a network-based encoder (e.g., the encoder portion of the network-based auto-encoder), the output of which includes a second training dataset associated with the network-based decoder. The second training dataset may be based on the training inputs for the network-based auto-encoder. For example, the second training dataset may be the output of the encoder portion of the network-based auto-encoder. The UE may receive the second training dataset and use this information as inputs to a UE-based encoder associated with or otherwise corresponding to the network-based decoder. This technique may permit the UE to train its encoder to the decoder of the network entity, but without knowing or otherwise being aware of the model that the network entity has deployed for its network-based auto-encoder.
Aspects of the disclosure are initially described in the context of wireless communications systems. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts that relate to model updates with UE latent query.
The network entities 105 may be dispersed throughout a geographic area to form the wireless communications system 100 and may include devices in different forms or having different capabilities. In various examples, a network entity 105 may be referred to as a network element, a mobility element, a radio access network (RAN) node, or network equipment, among other nomenclature. In some examples, network entities 105 and UEs 115 may wirelessly communicate via one or more communication links 125 (e.g., a radio frequency (RF) access link). For example, a network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) over which the UEs 115 and the network entity 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area over which a network entity 105 and a UE 115 may support the communication of signals according to one or more radio access technologies (RATs).
The UEs 115 may be dispersed throughout a coverage area 110 of the wireless communications system 100, and each UE 115 may be stationary, or mobile, or both at different times. The UEs 115 may be devices in different forms or having different capabilities. Some example UEs 115 are illustrated in
As described herein, a node of the wireless communications system 100, which may be referred to as a network node, or a wireless node, may be a network entity 105 (e.g., any network entity described herein), a UE 115 (e.g., any UE described herein), a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, a node may be a UE 115. As another example, a node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In yet other aspects of this example, the first, second, and third nodes may be different relative to these examples. Similarly, reference to a UE 115, network entity 105, apparatus, device, computing system, or the like may include disclosure of the UE 115, network entity 105, apparatus, device, computing system, or the like being a node. For example, disclosure that a UE 115 is configured to receive information from a network entity 105 also discloses that a first node is configured to receive information from a second node.
In some examples, network entities 105 may communicate with the core network 130, or with one another, or both. For example, network entities 105 may communicate with the core network 130 via one or more backhaul communication links 120 (e.g., in accordance with an S1, N2, N3, or other interface protocol). In some examples, network entities 105 may communicate with one another via a backhaul communication link 120 (e.g., in accordance with an X2, Xn, or other interface protocol) either directly (e.g., directly between network entities 105) or indirectly (e.g., via a core network 130). In some examples, network entities 105 may communicate with one another via a midhaul communication link 162 (e.g., in accordance with a midhaul interface protocol) or a fronthaul communication link 168 (e.g., in accordance with a fronthaul interface protocol), or any combination thereof. The backhaul communication links 120, midhaul communication links 162, or fronthaul communication links 168 may be or include one or more wired links (e.g., an electrical link, an optical fiber link), one or more wireless links (e.g., a radio link, a wireless optical link), among other examples or various combinations thereof. A UE 115 may communicate with the core network 130 via a communication link 155.
One or more of the network entities 105 described herein may include or may be referred to as a base station 140 (e.g., a base transceiver station, a radio base station, an NR base station, an access point, a radio transceiver, a NodeB, an eNodeB (eNB), a next-generation NodeB or a giga-NodeB (either of which may be referred to as a gNB), a 5G NB, a next-generation eNB (ng-eNB), a Home NodeB, a Home eNodeB, or other suitable terminology). In some examples, a network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, standalone) base station architecture, which may be configured to utilize a protocol stack that is physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as a base station 140).
In some examples, a network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture), which may be configured to utilize a protocol stack that is physically or logically distributed among two or more network entities 105, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance), or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN)). For example, a network entity 105 may include one or more of a central unit (CU) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN Intelligent Controller (RIC) 175 (e.g., a Near-Real Time RIC (Near-RT RIC), a Non-Real Time RIC (Non-RT RIC)), a Service Management and Orchestration (SMO) 180 system, or any combination thereof. An RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmission reception point (TRP). One or more components of the network entities 105 in a disaggregated RAN architecture may be co-located, or one or more components of the network entities 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 of a disaggregated RAN architecture may be implemented as virtual units (e.g., a virtual CU (VCU), a virtual DU (VDU), a virtual RU (VRU)).
The split of functionality between a CU 160, a DU 165, and an RU 170 is flexible and may support different functionalities depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, RF functions, and any combinations thereof) are performed at a CU 160, a DU 165, or an RU 170. For example, a functional split of a protocol stack may be employed between a CU 160 and a DU 165 such that the CU 160 may support one or more layers of the protocol stack and the DU 165 may support one or more different layers of the protocol stack. In some examples, the CU 160 may host upper protocol layer (e.g., layer 3 (L3), layer 2 (L2)) functionality and signaling (e.g., Radio Resource Control (RRC), service data adaption protocol (SDAP), Packet Data Convergence Protocol (PDCP)). The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160. Additionally, or alternatively, a functional split of the protocol stack may be employed between a DU 165 and an RU 170 such that the DU 165 may support one or more layers of the protocol stack and the RU 170 may support one or more different layers of the protocol stack. The DU 165 may support one or multiple different cells (e.g., via one or more RUs 170). In some cases, a functional split between a CU 160 and a DU 165, or between a DU 165 and an RU 170 may be within a protocol layer (e.g., some functions for a protocol layer may be performed by one of a CU 160, a DU 165, or an RU 170, while other functions of the protocol layer are performed by a different one of the CU 160, the DU 165, or the RU 170). A CU 160 may be functionally split further into CU control plane (CU-CP) and CU user plane (CU-UP) functions. A CU 160 may be connected to one or more DUs 165 via a midhaul communication link 162 (e.g., F1, F1-c, F1-u), and a DU 165 may be connected to one or more RUs 170 via a fronthaul communication link 168 (e.g., open fronthaul (FH) interface). In some examples, a midhaul communication link 162 or a fronthaul communication link 168 may be implemented in accordance with an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 105 that are in communication via such communication links.
In wireless communications systems (e.g., wireless communications system 100), infrastructure and spectral resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, providing an IAB network architecture (e.g., to a core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as a donor entity or an IAB donor. One or more DUs 165 or one or more RUs 170 may be partially controlled by one or more CUs 160 associated with a donor network entity 105 (e.g., a donor base station 140). The one or more donor network entities 105 (e.g., IAB donors) may be in communication with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links (e.g., backhaul communication links 120). IAB nodes 104 may include an IAB mobile termination (IAB-MT) controlled (e.g., scheduled) by DUs 165 of a coupled IAB donor. An IAB-MT may include an independent set of antennas for relay of communications with UEs 115, or may share the same antennas (e.g., of an RU 170) of an IAB node 104 used for access via the DU 165 of the IAB node 104 (e.g., referred to as virtual IAB-MT (vIAB-MT)). In some examples, the IAB nodes 104 may include DUs 165 that support communication links with additional entities (e.g., IAB nodes 104, UEs 115) within the relay chain or configuration of the access network (e.g., downstream). In such cases, one or more components of the disaggregated RAN architecture (e.g., one or more IAB nodes 104 or components of IAB nodes 104) may be configured to operate according to the techniques described herein.
For instance, an access network (AN) or RAN may include communications between access nodes (e.g., an IAB donor), IAB nodes 104, and one or more UEs 115. The IAB donor may facilitate connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130). That is, an IAB donor may refer to a RAN node with a wired or wireless connection to core network 130. The IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170), in which case the CU 160 may communicate with the core network 130 via an interface (e.g., a backhaul link). IAB donor and IAB nodes 104 may communicate via an F1 interface according to a protocol that defines signaling messages (e.g., an F1 AP protocol). Additionally, or alternatively, the CU 160 may communicate with the core network via an interface, which may be an example of a portion of backhaul link, and may communicate with other CUs 160 (e.g., a CU 160 associated with an alternative IAB donor) via an Xn-C interface, which may be an example of a portion of a backhaul link.
An IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UEs 115, wireless self-backhauling capabilities). A DU 165 may act as a distributed scheduling node towards child nodes associated with the IAB node 104, and the IAB-MT may act as a scheduled node towards parent nodes associated with the IAB node 104. That is, an IAB donor may be referred to as a parent node in communication with one or more child nodes (e.g., an IAB donor may relay transmissions for UEs through one or more other IAB nodes 104). Additionally, or alternatively, an IAB node 104 may also be referred to as a parent node or a child node to other IAB nodes 104, depending on the relay chain or configuration of the AN. Therefore, the IAB-MT entity of IAB nodes 104 may provide a Uu interface for a child IAB node 104 to receive signaling from a parent IAB node 104, and the DU interface (e.g., DUs 165) may provide a Uu interface for a parent IAB node 104 to signal to a child IAB node 104 or UE 115.
For example, IAB node 104 may be referred to as a parent node that supports communications for a child IAB node, or referred to as a child IAB node associated with an IAB donor, or both. The IAB donor may include a CU 160 with a wired or wireless connection (e.g., a backhaul communication link 120) to the core network 130 and may act as parent node to IAB nodes 104. For example, the DU 165 of IAB donor may relay transmissions to UEs 115 through IAB nodes 104, or may directly signal transmissions to a UE 115, or both. The CU 160 of IAB donor may signal communication link establishment via an F1 interface to IAB nodes 104, and the IAB nodes 104 may schedule transmissions (e.g., transmissions to the UEs 115 relayed from the IAB donor) through the DUs 165. That is, data may be relayed to and from IAB nodes 104 via signaling via an NR Uu interface to MT of the IAB node 104. Communications with IAB node 104 may be scheduled by a DU 165 of IAB donor and communications with IAB node 104 may be scheduled by DU 165 of IAB node 104.
In the case of the techniques described herein applied in the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture may be configured to support model updates with UE latent query as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally, or alternatively, be performed by one or more components of the disaggregated RAN architecture (e.g., IAB nodes 104, DUs 165, CUs 160, RUs 170, RIC 175, SMO 180).
A UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where the “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples. A UE 115 may also include or may be referred to as a personal electronic device such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, a UE 115 may include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which may be implemented in various objects such as appliances, or vehicles, meters, among other examples.
The UEs 115 described herein may be able to communicate with various types of devices, such as other UEs 115 that may sometimes act as relays as well as the network entities 105 and the network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, among other examples, as shown in
The UEs 115 and the network entities 105 may wirelessly communicate with one another via one or more communication links 125 (e.g., an access link) using resources associated with one or more carriers. The term “carrier” may refer to a set of RF spectrum resources having a defined physical layer structure for supporting the communication links 125. For example, a carrier used for a communication link 125 may include a portion of a RF spectrum band (e.g., a bandwidth part (BWP)) that is operated according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling that coordinates operation for the carrier, user data, or other signaling. The wireless communications system 100 may support communication with a UE 115 using carrier aggregation or multi-carrier operation. A UE 115 may be configured with multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation may be used with both frequency division duplexing (FDD) and time division duplexing (TDD) component carriers. Communication between a network entity 105 and other devices may refer to communication between the devices and any portion (e.g., entity, sub-entity) of a network entity 105. For example, the terms “transmitting,” “receiving,” or “communicating,” when referring to a network entity 105, may refer to any portion of a network entity 105 (e.g., a base station 140, a CU 160, a DU 165, a RU 170) of a RAN communicating with another device (e.g., directly or via one or more other network entities 105).
In some examples, such as in a carrier aggregation configuration, a carrier may also have acquisition signaling or control signaling that coordinates operations for other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (E-UTRA) absolute RF channel number (EARFCN)) and may be identified according to a channel raster for discovery by the UEs 115. A carrier may be operated in a standalone mode, in which case initial acquisition and connection may be conducted by the UEs 115 via the carrier, or the carrier may be operated in a non-standalone mode, in which case a connection is anchored using a different carrier (e.g., of the same or a different radio access technology).
The communication links 125 shown in the wireless communications system 100 may include downlink transmissions (e.g., forward link transmissions) from a network entity 105 to a UE 115, uplink transmissions (e.g., return link transmissions) from a UE 115 to a network entity 105, or both, among other configurations of transmissions. Carriers may carry downlink or uplink communications (e.g., in an FDD mode) or may be configured to carry downlink and uplink communications (e.g., in a TDD mode).
A carrier may be associated with a particular bandwidth of the RF spectrum and, in some examples, the carrier bandwidth may be referred to as a “system bandwidth” of the carrier or the wireless communications system 100. For example, the carrier bandwidth may be one of a set of bandwidths for carriers of a particular radio access technology (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz)). Devices of the wireless communications system 100 (e.g., the network entities 105, the UEs 115, or both) may have hardware configurations that support communications using a particular carrier bandwidth or may be configurable to support communications using one of a set of carrier bandwidths. In some examples, the wireless communications system 100 may include network entities 105 or UEs 115 that support concurrent communications using carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured for operating using portions (e.g., a sub-band, a BWP) or all of a carrier bandwidth.
Signal waveforms transmitted via a carrier may be made up of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system employing MCM techniques, a resource element may refer to resources of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier, in which case the symbol period and subcarrier spacing may be inversely related. The quantity of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both), such that a relatively higher quantity of resource elements (e.g., in a transmission duration) and a relatively higher order of a modulation scheme may correspond to a relatively higher rate of communication. A wireless communications resource may refer to a combination of an RF spectrum resource, a time resource, and a spatial resource (e.g., a spatial layer, a beam), and the use of multiple spatial resources may increase the data rate or data integrity for communications with a UE 115.
One or more numerologies for a carrier may be supported, and a numerology may include a subcarrier spacing (Δf) and a cyclic prefix. A carrier may be divided into one or more BWPs having the same or different numerologies. In some examples, a UE 115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time and communications for the UE 115 may be restricted to one or more active BWPs.
The time intervals for the network entities 105 or the UEs 115 may be expressed in multiples of a basic time unit which may, for example, refer to a sampling period of Ts=1/(Δfmax·Nf) seconds, for which Δfmax may represent a supported subcarrier spacing, and Ne may represent a supported discrete Fourier transform (DFT) size. Time intervals of a communications resource may be organized according to radio frames each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).
Each frame may include multiple consecutively-numbered subframes or slots, and each subframe or slot may have the same duration. In some examples, a frame may be divided (e.g., in the time domain) into subframes, and each subframe may be further divided into a quantity of slots. Alternatively, each frame may include a variable quantity of slots, and the quantity of slots may depend on subcarrier spacing. Each slot may include a quantity of symbol periods (e.g., depending on the length of the cyclic prefix prepended to each symbol period). In some wireless communications systems 100, a slot may further be divided into multiple mini-slots associated with one or more symbols. Excluding the cyclic prefix, each symbol period may be associated with one or more (e.g., Nf) sampling periods. The duration of a symbol period may depend on the subcarrier spacing or frequency band of operation.
A subframe, a slot, a mini-slot, or a symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communications system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., a quantity of symbol periods in a TTI) may be variable. Additionally, or alternatively, the smallest scheduling unit of the wireless communications system 100 may be dynamically selected (e.g., in bursts of shortened TTIs (STTIs)).
Physical channels may be multiplexed for communication using a carrier according to various techniques. A physical control channel and a physical data channel may be multiplexed for signaling via a downlink carrier, for example, using one or more of time division multiplexing (TDM) techniques, frequency division multiplexing (FDM) techniques, or hybrid TDM-FDM techniques. A control region (e.g., a control resource set (CORESET)) for a physical control channel may be defined by a set of symbol periods and may extend across the system bandwidth or a subset of the system bandwidth of the carrier. One or more control regions (e.g., CORESETs) may be configured for a set of the UEs 115. For example, one or more of the UEs 115 may monitor or search control regions for control information according to one or more search space sets, and each search space set may include one or multiple control channel candidates in one or more aggregation levels arranged in a cascaded manner. An aggregation level for a control channel candidate may refer to an amount of control channel resources (e.g., control channel elements (CCEs)) associated with encoded information for a control information format having a given payload size. Search space sets may include common search space sets configured for sending control information to multiple UEs 115 and UE-specific search space sets for sending control information to a specific UE 115.
A network entity 105 may provide communication coverage via one or more cells, for example a macro cell, a small cell, a hot spot, or other types of cells, or any combination thereof. The term “cell” may refer to a logical communication entity used for communication with a network entity 105 (e.g., using a carrier) and may be associated with an identifier for distinguishing neighboring cells (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or others). In some examples, a cell also may refer to a coverage area 110 or a portion of a coverage area 110 (e.g., a sector) over which the logical communication entity operates. Such cells may range from smaller areas (e.g., a structure, a subset of structure) to larger areas depending on various factors such as the capabilities of the network entity 105. For example, a cell may be or include a building, a subset of a building, or exterior spaces between or overlapping with coverage areas 110, among other examples.
A macro cell generally covers a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by the UEs 115 with service subscriptions with the network provider supporting the macro cell. A small cell may be associated with a lower-powered network entity 105 (e.g., a lower-powered base station 140), as compared with a macro cell, and a small cell may operate using the same or different (e.g., licensed, unlicensed) frequency bands as macro cells. Small cells may provide unrestricted access to the UEs 115 with service subscriptions with the network provider or may provide restricted access to the UEs 115 having an association with the small cell (e.g., the UEs 115 in a closed subscriber group (CSG), the UEs 115 associated with users in a home or office). A network entity 105 may support one or multiple cells and may also support communications via the one or more cells using one or multiple component carriers.
In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access for different types of devices.
In some examples, a network entity 105 (e.g., a base station 140, an RU 170) may be movable and therefore provide communication coverage for a moving coverage area 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, the overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communications system 100 may include, for example, a heterogeneous network in which different types of the network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.
The wireless communications system 100 may support synchronous or asynchronous operation. For synchronous operation, network entities 105 (e.g., base stations 140) may have similar frame timings, and transmissions from different network entities 105 may be approximately aligned in time. For asynchronous operation, network entities 105 may have different frame timings, and transmissions from different network entities 105 may, in some examples, not be aligned in time. The techniques described herein may be used for either synchronous or asynchronous operations.
Some UEs 115, such as MTC or IoT devices, may be low cost or low complexity devices and may provide for automated communication between machines (e.g., via Machine-to-Machine (M2M) communication). M2M communication or MTC may refer to data communication technologies that allow devices to communicate with one another or a network entity 105 (e.g., a base station 140) without human intervention. In some examples, M2M communication or MTC may include communications from devices that integrate sensors or meters to measure or capture information and relay such information to a central server or application program that uses the information or presents the information to humans interacting with the application program. Some UEs 115 may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based business charging.
Some UEs 115 may be configured to employ operating modes that reduce power consumption, such as half-duplex communications (e.g., a mode that supports one-way communication via transmission or reception, but not transmission and reception concurrently). In some examples, half-duplex communications may be performed at a reduced peak rate. Other power conservation techniques for the UEs 115 include entering a power saving deep sleep mode when not engaging in active communications, operating using a limited bandwidth (e.g., according to narrowband communications), or a combination of these techniques. For example, some UEs 115 may be configured for operation using a narrowband protocol type that is associated with a defined portion or range (e.g., set of subcarriers or resource blocks (RBs)) within a carrier, within a guard-band of a carrier, or outside of a carrier.
The wireless communications system 100 may be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communications system 100 may be configured to support ultra-reliable low-latency communications (URLLC). The UEs 115 may be designed to support ultra-reliable, low-latency, or critical functions. Ultra-reliable communications may include private communication or group communication and may be supported by one or more services such as push-to-talk, video, or data. Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, and ultra-reliable low-latency may be used interchangeably herein.
In some examples, a UE 115 may be configured to support communicating directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., in accordance with a peer-to-peer (P2P), D2D, or sidelink protocol). In some examples, one or more UEs 115 of a group that are performing D2D communications may be within the coverage area 110 of a network entity 105 (e.g., a base station 140, an RU 170), which may support aspects of such D2D communications being configured by (e.g., scheduled by) the network entity 105. In some examples, one or more UEs 115 of such a group may be outside the coverage area 110 of a network entity 105 or may be otherwise unable to or not configured to receive transmissions from a network entity 105. In some examples, groups of the UEs 115 communicating via D2D communications may support a one-to-many (1:M) system in which each UE 115 transmits to each of the other UEs 115 in the group. In some examples, a network entity 105 may facilitate the scheduling of resources for D2D communications. In some other examples, D2D communications may be carried out between the UEs 115 without an involvement of a network entity 105.
In some systems, a D2D communication link 135 may be an example of a communication channel, such as a sidelink communication channel, between vehicles (e.g., UEs 115). In some examples, vehicles may communicate using vehicle-to-everything (V2X) communications, vehicle-to-vehicle (V2V) communications, or some combination of these. A vehicle may signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information relevant to a V2X system. In some examples, vehicles in a V2X system may communicate with roadside infrastructure, such as roadside units, or with the network via one or more network nodes (e.g., network entities 105, base stations 140, RUs 170) using vehicle-to-network (V2N) communications, or with both.
The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or 5G core (5GC), which may include at least one control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) and at least one user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)). The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management for the UEs 115 served by the network entities 105 (e.g., base stations 140) associated with the core network 130. User IP packets may be transferred through the user plane entity, which may provide IP address allocation as well as other functions. The user plane entity may be connected to IP services 150 for one or more network operators. The IP services 150 may include access to the Internet, Intranet(s), an IP Multimedia Subsystem (IMS), or a Packet-Switched Streaming Service.
The wireless communications system 100 may operate using one or more frequency bands, which may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or decimeter band because the wavelengths range from approximately one decimeter to one meter in length. UHF waves may be blocked or redirected by buildings and environmental features, which may be referred to as clusters, but the waves may penetrate structures sufficiently for a macro cell to provide service to the UEs 115 located indoors. Communications using UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) compared to communications using the smaller frequencies and longer waves of the high frequency (HF) or very high frequency (VHF) portion of the spectrum below 300 MHz.
The wireless communications system 100 may also operate using a super high frequency (SHF) region, which may be in the range of 3 GHz to 30 GHz, also known as the centimeter band, or using an extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz), also known as the millimeter band. In some examples, the wireless communications system 100 may support millimeter wave (mmW) communications between the UEs 115 and the network entities 105 (e.g., base stations 140, RUs 170), and EHF antennas of the respective devices may be smaller and more closely spaced than UHF antennas. In some examples, such techniques may facilitate using antenna arrays within a device. The propagation of EHF transmissions, however, may be subject to even greater attenuation and shorter range than SHF or UHF transmissions. The techniques disclosed herein may be employed across transmissions that use one or more different frequency regions, and designated use of bands across these frequency regions may differ by country or regulating body.
The wireless communications system 100 may utilize both licensed and unlicensed RF spectrum bands. For example, the wireless communications system 100 may employ License Assisted Access (LAA), LTE-Unlicensed (LTE-U) radio access technology, or NR technology using an unlicensed band such as the 5 GHz industrial, scientific, and medical (ISM) band. While operating using unlicensed RF spectrum bands, devices such as the network entities 105 and the UEs 115 may employ carrier sensing for collision detection and avoidance. In some examples, operations using unlicensed bands may be based on a carrier aggregation configuration in conjunction with component carriers operating using a licensed band (e.g., LAA). Operations using unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among other examples.
A network entity 105 (e.g., a base station 140, an RU 170) or a UE 115 may be equipped with multiple antennas, which may be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of a network entity 105 or a UE 115 may be located within one or more antenna arrays or antenna panels, which may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly, such as an antenna tower. In some examples, antennas or antenna arrays associated with a network entity 105 may be located at diverse geographic locations. A network entity 105 may include an antenna array with a set of rows and columns of antenna ports that the network entity 105 may use to support beamforming of communications with a UE 115. Likewise, a UE 115 may include one or more antenna arrays that may support various MIMO or beamforming operations. Additionally, or alternatively, an antenna panel may support RF beamforming for a signal transmitted via an antenna port.
The network entities 105 or the UEs 115 may use MIMO communications to exploit multipath signal propagation and increase spectral efficiency by transmitting or receiving multiple signals via different spatial layers. Such techniques may be referred to as spatial multiplexing. The multiple signals may, for example, be transmitted by the transmitting device via different antennas or different combinations of antennas. Likewise, the multiple signals may be received by the receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry information associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers may be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO), for which multiple spatial layers are transmitted to the same receiving device, and multiple-user MIMO (MU-MIMO), for which multiple spatial layers are transmitted to multiple devices.
Beamforming, which may also be referred to as spatial filtering, directional transmission, or directional reception, is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a network entity 105, a UE 115) to shape or steer an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining the signals communicated via antenna elements of an antenna array such that some signals propagating along particular orientations with respect to an antenna array experience constructive interference while others experience destructive interference. The adjustment of signals communicated via the antenna elements may include a transmitting device or a receiving device applying amplitude offsets, phase offsets, or both to signals carried via the antenna elements associated with the device. The adjustments associated with each of the antenna elements may be defined by a beamforming weight set associated with a particular orientation (e.g., with respect to the antenna array of the transmitting device or receiving device, or with respect to some other orientation).
A network entity 105 or a UE 115 may use beam sweeping techniques as part of beamforming operations. For example, a network entity 105 (e.g., a base station 140, an RU 170) may use multiple antennas or antenna arrays (e.g., antenna panels) to conduct beamforming operations for directional communications with a UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be transmitted by a network entity 105 multiple times along different directions. For example, the network entity 105 may transmit a signal according to different beamforming weight sets associated with different directions of transmission. Transmissions along different beam directions may be used to identify (e.g., by a transmitting device, such as a network entity 105, or by a receiving device, such as a UE 115) a beam direction for later transmission or reception by the network entity 105.
Some signals, such as data signals associated with a particular receiving device, may be transmitted by transmitting device (e.g., a transmitting network entity 105, a transmitting UE 115) along a single beam direction (e.g., a direction associated with the receiving device, such as a receiving network entity 105 or a receiving UE 115). In some examples, the beam direction associated with transmissions along a single beam direction may be determined based on a signal that was transmitted along one or more beam directions. For example, a UE 115 may receive one or more of the signals transmitted by the network entity 105 along different directions and may report to the network entity 105 an indication of the signal that the UE 115 received with a highest signal quality or an otherwise acceptable signal quality.
In some examples, transmissions by a device (e.g., by a network entity 105 or a UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or beamforming to generate a combined beam for transmission (e.g., from a network entity 105 to a UE 115). The UE 115 may report feedback that indicates precoding weights for one or more beam directions, and the feedback may correspond to a configured set of beams across a system bandwidth or one or more sub-bands. The network entity 105 may transmit a reference signal (e.g., a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS)), which may be precoded or unprecoded. The UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel type codebook, a linear combination type codebook, a port selection type codebook). Although these techniques are described with reference to signals transmitted along one or more directions by a network entity 105 (e.g., a base station 140, an RU 170), a UE 115 may employ similar techniques for transmitting signals multiple times along different directions (e.g., for identifying a beam direction for subsequent transmission or reception by the UE 115) or for transmitting a signal along a single direction (e.g., for transmitting data to a receiving device).
A receiving device (e.g., a UE 115) may perform reception operations in accordance with multiple receive configurations (e.g., directional listening) when receiving various signals from a transmitting device (e.g., a network entity 105), such as synchronization signals, reference signals, beam selection signals, or other control signals. For example, a receiving device may perform reception in accordance with multiple receive directions by receiving via different antenna subarrays, by processing received signals according to different antenna subarrays, by receiving according to different receive beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or by processing received signals according to different receive beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as “listening” according to different receive configurations or receive directions. In some examples, a receiving device may use a single receive configuration to receive along a single beam direction (e.g., when receiving a data signal). The single receive configuration may be aligned along a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have a highest signal strength, highest signal-to-noise ratio (SNR), or otherwise acceptable signal quality based on listening according to multiple beam directions).
The wireless communications system 100 may be a packet-based network that operates according to a layered protocol stack. In the user plane, communications at the bearer or PDCP layer may be IP-based. An RLC layer may perform packet segmentation and reassembly to communicate via logical channels. A MAC layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer also may implement error detection techniques, error correction techniques, or both to support retransmissions to improve link efficiency. In the control plane, an RRC layer may provide establishment, configuration, and maintenance of an RRC connection between a UE 115 and a network entity 105 or a core network 130 supporting radio bearers for user plane data. A PHY layer may map transport channels to physical channels.
The UEs 115 and the network entities 105 may support retransmissions of data to increase the likelihood that data is received successfully. Hybrid automatic repeat request (HARQ) feedback is one technique for increasing the likelihood that data is received correctly via a communication link (e.g., a communication link 125, a D2D communication link 135). HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., automatic repeat request (ARQ)). HARQ may improve throughput at the MAC layer in poor radio conditions (e.g., low signal-to-noise conditions). In some examples, a device may support same-slot HARQ feedback, in which case the device may provide HARQ feedback in a specific slot for data received via a previous symbol in the slot. In some other examples, the device may provide HARQ feedback in a subsequent slot, or according to some other time interval.
A UE 115 may transmit a first training dataset associated with a network-based auto-encoder, or an indication thereof, where the first training dataset comprises one or more channel metrics obtained by the UE 115, a precoding metric, or both. The UE 115 may receive, in response to transmitting the first training dataset or the indication thereof, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
A network entity 105 may receive or otherwise obtain, from a UE 115, a first training dataset associated with a network-based auto-encoder, or an indication thereof, where the first training dataset comprises one or more channel metrics obtained by the UE 115, a precoding metric, or both. The network entity 105 may transmit or otherwise provide for output, in response to obtaining the first training dataset or the indication thereof, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based at least in part on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Wireless networks may use machine learning or non-machine learning algorithms to improve network performance. One example may include the encoding/decoding operations of the UE and the network entity that are based on downlink channel estimates. For example, a nominal or reference encoder function may be implemented at or by the UE while a nominal or reference decoder function may be implemented at or by the network entity. The UE encoder may measure or otherwise obtain channel metrics (e.g., downlink channel measurements or estimates) that are provided to or otherwise used by the encoder for encoding data for transmission to the network entity (e.g., via a data or control channel). The output of the UE encoder is transmitted using the wireless medium to the network entity and input into the network decoder. By way of non-limiting example, the UE encoder output may be a compressed channel state feedback, which is input into the network decoder. The network decoder outputs a reconstructed channel state feedback, such as using precoding vector(s), channel vector(s), or other precoding related information. In such cross-node machine learning techniques, the neural network is split into two portions (e.g., the encoder of the UE and the decoder of the network entity).
In some examples, this training of the UE encoder/network decoder may be performed offline using servers associated with the UE and network. For example, in multi-vendor training scenarios each vendor (e.g., the UE vendor and the network entity vendor) may have its own server that participates in the offline training. For example, the UE server 215 may be associated with a vender of the UE 205 and the network server 220 may be associated with the network entity 210. The UE vender server(s) may communicate with the network server(s) during the training using server-to-server connections. For example, the UE server 215 and the network server 220 may exchange various training dataset(s) associated with the UE 205 and the network entity 210 during the training process.
Different training cases may be used for such multi-vendor training procedures. A first case may be a type 3 training that is performed between one network part model (e.g., the network decoder) and one UE part model (e.g., the UE encoder). A second case may be a UE-first training techniques where type 3 training is performed between one network part model and N>1 UE part models. A third case may be a network-first training where type 3 training is performed between one UE part model and N>1 network part models.
In some aspects, the training scheme may include a sequential training that is driven be the network entity. In some training models, the specific machine learning training model may be the same between the UE encoder and the network decoder, which may include the devices sharing model information and parameters. In multi-vendor training models, this approach may mean that each network vendor would share their modeling related information with the other network vendor, which may be proprietary information protected by the vendor. In sequential training, this scheme enables the UE and the network entity to keep their training models private (e.g., each vendor is unaware which model(s) the other vendors are using). This approach differs from the one-sided concurrent training techniques discussed above.
For example, in the network-first sequential training approach the network decoder (e.g., a network-based decoder) may be trained first at the network server 220 with an encoder that is chosen or otherwise selected by the network entity. This may include a network-based auto-encoder being implemented at the network entity 210, at the network server 220, or both. The network-based auto-encoder may include a network-based encoder and a network-based decoder that are used to train the decoder of the network entity 210.
More particularly, network-based auto-encoder may operate by inputting channel metrics or precoding metrics (Vin or simply V) estimated or otherwise associated with the channel. The output of the network-based encoder may include a compressed channel state feedback information (e.g., precoding vectors or channel vectors, which may be referred to as z) that is provided to a loss function for evaluation of the training iteration. The loss function evaluation evaluates the accuracy of the network-based decoder in view of the channel metrics input to the network-based encoder. For example, the output of the loss function may update various weights or other parameters of the network-based encoder, the network-based decoder, or both, to improve the training output (e.g., to provide more accurate z). In the network-driven sequential training scenario, the network server 220 may transmit or otherwise provide the output of the network-based decoder portion of the network-based auto-encoder (e.g., z) along with the training inputs (e.g., V) to the UE server 215. The UE server 215 may use (z, V) as a training input dataset to train the encoder of the UE, which results in the encoder of the UE being trained to the decoder of the network entity.
To avoid model information being shared, some training techniques may include one-sided models where the network entity indicates some criteria (e.g., such as the time, zone, cell, and other criteria) where the UE is to collect data (e.g., measure reference signals or otherwise obtain channel metrics). The “model” being used by the network entity may be identified based on either the criteria indicated to the UE or based on an identifier (e.g., a dataset identifier). The UE may train a model (e.g., the UE encoder) using this collected data (e.g., the channel metrics). Although the network knows the network-side settings applicable to the model, this information is not revealed to the UE and the network may use this trained model during inference operations with respect to the UE (e.g., for power aware scheduling).
Aspects of the techniques described herein extend this technique to two-sided models with network-first type 3 training. For example, the techniques described herein discuss various procedural aspects of the two-sided model training, which includes over-the-air signaling. Aspects of the techniques described herein provide for a UE (e.g., a new UE side vendor) that may attempt to create or otherwise initiate an encoder that is compatible with a decoder that is already in use (e.g., the decoder of the network entity). In some aspects, the network decoder may be considered as a network setting, such as a downtilt of an antenna panel of the network.
That is, precoding operations are generally performed by a transmitting device to optimize the received signals at the receiving device while reducing interference. Precoding includes the transmitting device pre-processing the signal being transmitted to improve performance and increase spectral efficiency. This may include the transmitting device selecting and configuring various beams, antenna panels, or other spatial or non-spatial features for the transmission to improve the likelihood of reception. The inputs to the precoding operations may include or be based on the channel performance metrics of the wireless channel between the transmitting device (or the antenna panel(s) of the transmitting device) and the receiving device. The precoding vectors (or channel vectors) may be designed or selected to optimize the transmissions to the receiving device using the wireless channel. Accordingly, references to an encoder/decoder may refer to various aspects of the precoding operations or training, such as antenna panel setting(s), beam direction(s), or other spatial-related information associated with the transmissions to the receiving device, which may be determined using the precoding vector(s) or channel vector(s).
In some aspects, the techniques described herein may include the network entity 210 indicating to the UE 205 information associated with the network-based auto-encoder, such as the time, zone, cell, or other related information (e.g., the criteria) where the network-based decoder is active during the model development phase. The information may include various communication parameters (such as the time, zone, cell, or other related criteria) or an identifier associated with the network-based auto-encoder.
For example, the UE 205 may transmit or otherwise provide for output (and the network entity 210 may receive or otherwise obtain) an indication of a first training dataset associated with the network-based auto-encoder. The first training dataset may be used as first training inputs for the network-based auto-encoder. For example, the first training inputs may include or otherwise be based on channel metric(s) obtained by the UE 205 (e.g., measured or estimated). The first training inputs of the first training dataset may be an example of the encoder input V, as discussed above. The first training inputs of the first training dataset may include the channel metric(s), a precoding metric, both metrics, or other related metrics. Accordingly, the UE 205 may provide the channel metric(s) information to the network entity 210 in the first training dataset.
In some examples, the UE 205 may send the first training dataset (V) without compression (e.g., as a non-compressed dataset) using layer two (L2) signaling. For example, the first training dataset may be sent in a medium access control-control element (MAC-CE) signal. In some examples, the UE 205 may send the first training dataset (V) with at least some degree of compression (e.g., as a compressed dataset). For example, the UE 205 may use or otherwise apply a compression algorithm to the first training dataset before transmission. In some examples, the compression algorithm may be a limited compression algorithm (e.g., introducing a relatively small amount of compression to reduce the amount of data bits being used to convey the first training dataset). One example of a compression algorithm may include various data compression techniques designed to reduce the number of bits being communicated while maintaining the substance of the information being conveyed. Some example compression algorithms may include a machine learning based algorithm or a non-machine learning algorithm. As one non-limiting example, a non-machine learning algorithm may include using a high resolution eTypeII precoder feedback technique. An example of a machine learning algorithm may include a machine learning method with a low compression ratio.
In some examples, the network entity 210 may input the first training dataset into a network-based encoder (e.g., a nominal or reference encoder portion of the network-based auto-encoder). The network-based encoder analyze the channel metrics and output a second training dataset, which may correspond to z, as discussed above. For example, the second training dataset (z) may include various precoding related information, such as precoding vector(s), channel vector(s), or other precoding related information. Thus, the network entity 210 may use the channel metrics/precoding metrics (V) as training inputs for the network-based nominal or reference encoder, which outputs the second training dataset (z) corresponding to the precoding vectors, channel vectors, or other related precoding information.
In some examples, the network entity 210 may identify or otherwise determine the second training dataset in cooperation with the network server 220. For example, the network entity 210 may transmit or otherwise provide the first training dataset to the network server 220, which responds by transmitting or otherwise providing the second training dataset to the network entity 210. That is, some or all of the network-based auto-encoder may be implemented at or by the network server 220.
The network entity 210 may transmit or otherwise provide for output (and the UE 205 may receive or otherwise obtain) the second training dataset associated with the network-based auto-encoder. As discussed, the second training dataset may be based on the first training inputs indicated in the first training dataset (V). The second training dataset may be used for training a UE-based encoder that corresponds to the network-based auto-encoder (e.g., using the information, such as the criteria or identifier).
In some examples, this may include the UE 205 inputting the second training dataset into an encoder of or otherwise associated with the UE 205 to train the UE encoder to the network entity decoder. For example, the UE 205 may train the UE-based encoder using the second training dataset obtained from the network entity 210. This may train the encoder of the UE 205 to the decoder of the network entity 210, but without having to share the model related parameters being used by the respective vendors.
In some example, the UE 205 may train the UE-based encoder in cooperation with the UE server 215. For example, the UE 205 may transmit or otherwise provide the second training dataset to the UE server 215. The UE server 215 may train the UE-based encoder (e.g., using its own UE-based auto-encoder associated with the UE 205) and output an updated encoder model to the UE 205 for the UE-based encoder. The updated encoder model may server as training inputs or other operating parameters (e.g., precoding vector(s) or channel vector(s)) used to train the UE-based encoder of the UE 205 to the decoder of the network entity 210.
Accordingly, the techniques described herein provide for the network entity 210 to send the second training dataset (z) (e.g., a latent representation based on the network reference encoder) corresponding to the first training dataset (e.g., as channel state information inputs, (V)) received from the UE 205 via over-the-air signaling. More particularly, the described techniques may include the UE 205 measuring the channel metrics (V) precoder information based on CSI-RS measurements. The UE 205 may convey V to the network entity 210 using a legacy CSI mechanism or using a machine learning model. The network entity 210 may respond with the latent vector (z) for that (V), which was derived using the nominal encoder associated with the decoder of the network entity 210.
These techniques may provide for the UE 205 to continuously augment its dataset for training/fine-tuning of the UE encoder without the needing to reinitiate a new training procedure with the network vendor. The latent vector (z) coming from the network may ensure compatibility of the updated UE model, as needed.
At 315, the UE 305 may transmit or otherwise provide an indication of a first training dataset associated with the network-based auto-encoder. That is, the network entity 310 may have or otherwise be associated with a network-based auto-encoder. The network-based auto-encoder may be implemented at the network entity 310 or at a network server (e.g., a server of a vendor of the network entity 310) associated with the network entity 310.
The first training dataset may include, convey, or otherwise indicate first training inputs for the network-based auto-encoder. The first training inputs may be based on channel metrics (e.g., the channel metrics, precoding metrics, or both) obtained by the UE 305. For example, the UE 305 may measure one or more reference signal(s) (e.g., CSI-RS(s)) to obtain the channel metrics.
At 320, the network entity 310 may transmit or otherwise provide for output (and the UE 305 may receive or otherwise obtain) a second training dataset associated with the network-based auto-encoder. The second training dataset may be based on the first training inputs (e.g., from the first training dataset) and may be used to train a UE-based encoder that corresponds to or is otherwise associated with the network-based auto-encoder.
For example, the network entity 310 (or a network server associated with the network entity) may input the first training inputs into a network-based encoder portion of the network-based auto-encoder. The network-based encoder may use the first training inputs (e.g., the channel metrics, V) as inputs to identify, select, or otherwise determine precoding vectors or channel vectors to be used for communications using the channel between the UE 305 and the network entity 310. The precoding vectors or channel vectors may be provided to the UE 305 in the second training dataset.
The UE 305 (or a UE server associated with the UE 305) may use the second training dataset to train its encoder (e.g., a UE-based encoder). That is, the UE 305 use the first training inputs (e.g., the channel metrics) along with the precoding vectors or channel vectors from the second training dataset to train its encoder to the decoder of the network entity 310.
At 415, the network entity 410 may transmit or otherwise provide for output (and the UE 405 may receive or otherwise obtain) information associated with a network-based auto-encoder. For example, the network entity 410 may transmit an RRC configuration message to the UE 405 indicating or otherwise configuring CSI-RS resources for the UE 405 to measure. The CSI-RS resources may include time resources, frequency resources, spatial resources, or code resources that will be used to transmit the CSI-RS, which the UE 405 may monitor to receive the CSI-RS transmissions. The message may also indicate various information related to the machine learning channel state feedback reporting. For example, the message may indicate the criteria or identifier of a network-based auto-encoder of or otherwise associated with the network entity 410.
At 420, the UE 405 may monitor the CSI-RS resources indicated to in the information provided by the network entity 410. For example, the UE 405 may measure the CSI-RS transmissions to calculate, identify, or otherwise obtain channel metrics associated with the wireless channel between the UE 405 and the network entity 410.
At 425, the UE 405 may generate or otherwise determine input CSI to the encoder. That is, the UE 405 may generate the first training dataset (e.g., V) based on the channel metrics obtained by measuring the CSI-RS. The channel metrics or precoding metric may be used as the first training inputs for a network-based encoder portion of the network-based auto-encoder of or otherwise associated with the network entity 410.
At 430, the UE 405 may transmit or otherwise provide for output (and the network entity 410 may receive or otherwise obtain) the first training dataset (e.g., the input CSI (V)). The first training dataset may be associated with the network-based auto-encoder. For example, various criteria or identifier information (such as the CSI-RS resources configured for the UE 405, as well as other information) may be linked to or otherwise associated with the network-based auto-encoder of the network entity 410. In some aspects, the first training dataset may be transmitted as a compressed dataset (e.g., using a compression algorithm) or as a non-compressed dataset. In some aspects, the first training dataset may be transmitted to the network entity 410 using layer one (L1) or L2 signaling, such as in a MAC-CE.
At 435, the network entity 410 may generate compressed CSI (z) from its nominal encoder. That is, the network entity 410 (or a network server associated with the network entity 410) may input the first training inputs from the first training dataset into a network-based encoder portion of the network-based auto-encoder. The output of the network-based encoder may include the compressed CSI (z) that corresponds to a second training dataset. The second training set may include or otherwise be based on various precoding related parameters, such as precoding vectors, channel vectors, or related information.
At 440, the network entity 410 may transmit or otherwise provide for output (and the UE 405 may receive or otherwise obtain) the second training dataset (z) associated with the network-based auto-encoder. As discussed, the second training dataset may be based on the first training inputs carried in the first training dataset. The network entity 410 may transmit the second training dataset without compression and using L2 signaling, such as in a downlink MAC-CE.
At 445, the UE 405 may train the UE-based encoder to the network-based auto-encoder. For example, the UE 405 may use the first training dataset and the second training dataset to train its encoder (alone or in cooperation with a UE server associated with the UE 405) to the decoder of the network entity.
The receiver 510 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to model updates with UE latent query). Information may be passed on to other components of the device 505. The receiver 510 may utilize a single antenna or a set of multiple antennas.
The transmitter 515 may provide a means for transmitting signals generated by other components of the device 505. For example, the transmitter 515 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to model updates with UE latent query). In some examples, the transmitter 515 may be co-located with a receiver 510 in a transceiver module. The transmitter 515 may utilize a single antenna or a set of multiple antennas.
The communications manager 520, the receiver 510, the transmitter 515, or various combinations thereof or various components thereof may be examples of means for performing various aspects of model updates with UE latent query as described herein. For example, the communications manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be capable of performing one or more of the functions described herein.
In some examples, the communications manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry). The hardware may include at least one of a processor, a digital signal processor (DSP), a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure. In some examples, at least one processor and at least one memory coupled with the at least one processor may be configured to perform one or more of the functions described herein (e.g., by one or more processors, individually or collectively, executing instructions stored in the at least one memory).
Additionally, or alternatively, the communications manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in code (e.g., as communications management software or firmware) executed by at least one processor. If implemented in code executed by at least one processor, the functions of the communications manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure).
In some examples, the communications manager 520 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 510, the transmitter 515, or both. For example, the communications manager 520 may receive information from the receiver 510, send information to the transmitter 515, or be integrated in combination with the receiver 510, the transmitter 515, or both to obtain information, output information, or perform various other operations as described herein.
The communications manager 520 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 520 is capable of, configured to, or operable to support a means for transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The communications manager 520 is capable of, configured to, or operable to support a means for receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
By including or configuring the communications manager 520 in accordance with examples as described herein, the device 505 (e.g., at least one processor controlling or otherwise coupled with the receiver 510, the transmitter 515, the communications manager 520, or a combination thereof) may support techniques for two-sided machine learning model training using a network-first type 3 training that includes over-the-air signaling.
The receiver 610 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to model updates with UE latent query). Information may be passed on to other components of the device 605. The receiver 610 may utilize a single antenna or a set of multiple antennas.
The transmitter 615 may provide a means for transmitting signals generated by other components of the device 605. For example, the transmitter 615 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to model updates with UE latent query). In some examples, the transmitter 615 may be co-located with a receiver 610 in a transceiver module. The transmitter 615 may utilize a single antenna or a set of multiple antennas.
The device 605, or various components thereof, may be an example of means for performing various aspects of model updates with UE latent query as described herein. For example, the communications manager 620 may include a first dataset manager 625 a second dataset manager 630, or any combination thereof. The communications manager 620 may be an example of aspects of a communications manager 520 as described herein. In some examples, the communications manager 620, or various components thereof, may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 610, the transmitter 615, or both. For example, the communications manager 620 may receive information from the receiver 610, send information to the transmitter 615, or be integrated in combination with the receiver 610, the transmitter 615, or both to obtain information, output information, or perform various other operations as described herein.
The communications manager 620 may support wireless communications in accordance with examples as disclosed herein. The first dataset manager 625 is capable of, configured to, or operable to support a means for transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The second dataset manager 630 is capable of, configured to, or operable to support a means for receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
The communications manager 720 may support wireless communications in accordance with examples as disclosed herein. The first dataset manager 725 is capable of, configured to, or operable to support a means for transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The second dataset manager 730 is capable of, configured to, or operable to support a means for receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. In some examples, the training manager 735 is capable of, configured to, or operable to support a means for training the UE-based encoder using the second training dataset.
In some examples, the UE server manager 740 is capable of, configured to, or operable to support a means for transmitting the second training dataset to a UE server associated with the UE. In some examples, the UE server manager 740 is capable of, configured to, or operable to support a means for receiving an updated encoder model for the UE-based encoder from the UE server, where the updated encoder model is based on the second training dataset.
In some examples, the configuration manager 745 is capable of, configured to, or operable to support a means for receiving information associated with the network-based auto-encoder, where transmitting the indication of the first training dataset is based on the information. In some examples, the information associated with the network-based auto-encoder includes an identifier associated with the network-based auto-encoder. In some examples, the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE. In some examples, the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active. In some examples, the first training dataset is transmitted as a non-compressed dataset.
In some examples, the compression manager 750 is capable of, configured to, or operable to support a means for applying a compression algorithm to the first training dataset prior to transmission. In some examples, the compression algorithm includes at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm. In some examples, the first training dataset includes the one or more channel metrics, a precoding metric, or both. In some examples, the second training dataset includes an output of a network-based encoder portion of the network-based auto-encoder using the one or more channel metrics.
In some examples, the second dataset manager 730 is capable of, configured to, or operable to support a means for receiving an indication that the second training dataset is for training the UE-based encoder of the UE.
The I/O controller 810 may manage input and output signals for the device 805. The I/O controller 810 may also manage peripherals not integrated into the device 805. In some cases, the I/O controller 810 may represent a physical connection or port to an external peripheral. In some cases, the I/O controller 810 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. Additionally, or alternatively, the I/O controller 810 may represent or interact with a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the I/O controller 810 may be implemented as part of one or more processors, such as the at least one processor 840. In some cases, a user may interact with the device 805 via the I/O controller 810 or via hardware components controlled by the I/O controller 810.
In some cases, the device 805 may include a single antenna 825. However, in some other cases, the device 805 may have more than one antenna 825, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 815 may communicate bi-directionally, via the one or more antennas 825, wired, or wireless links as described herein. For example, the transceiver 815 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 815 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 825 for transmission, and to demodulate packets received from the one or more antennas 825. The transceiver 815, or the transceiver 815 and one or more antennas 825, may be an example of a transmitter 515, a transmitter 615, a receiver 510, a receiver 610, or any combination thereof or component thereof, as described herein.
The at least one memory 830 may include random access memory (RAM) and read-only memory (ROM). The at least one memory 830 may store computer-readable, computer-executable code 835 including instructions that, when executed by the at least one processor 840, cause the device 805 to perform various functions described herein. The code 835 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code 835 may not be directly executable by the at least one processor 840 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some cases, the at least one memory 830 may contain, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.
The at least one processor 840 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the at least one processor 840 may be configured to operate a memory array using a memory controller. In some other cases, a memory controller may be integrated into the at least one processor 840. The at least one processor 840 may be configured to execute computer-readable instructions stored in a memory (e.g., the at least one memory 830) to cause the device 805 to perform various functions (e.g., functions or tasks supporting model updates with UE latent query). For example, the device 805 or a component of the device 805 may include at least one processor 840 and at least one memory 830 coupled with or to the at least one processor 840, the at least one processor 840 and at least one memory 830 configured to perform various functions described herein. In some examples, the at least one processor 840 may include multiple processors and the at least one memory 830 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein. In some examples, the at least one processor 840 may be a component of a processing system, which may refer to a system (such as a series) of machines, circuitry (including, for example, one or both of processor circuitry (which may include the at least one processor 840) and memory circuitry (which may include the at least one memory 830)), or components, that receives or obtains inputs and processes the inputs to produce, generate, or obtain a set of outputs. The processing system may be configured to perform one or more of the functions described herein. For example, the at least one processor 840 or a processing system including the at least one processor 840 may be configured to, configurable to, or operable to cause the device 805 to perform one or more of the functions described herein. Further, as described herein, being “configured to,” being “configurable to,” and being “operable to” may be used interchangeably and may be associated with a capability, when executing code stored in the at least one memory 830 or otherwise, to perform one or more of the functions described herein.
The communications manager 820 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 820 is capable of, configured to, or operable to support a means for transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The communications manager 820 is capable of, configured to, or operable to support a means for receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
By including or configuring the communications manager 820 in accordance with examples as described herein, the device 805 may support techniques for two-sided machine learning model training using a network-first type 3 training that includes over-the-air signaling.
In some examples, the communications manager 820 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the transceiver 815, the one or more antennas 825, or any combination thereof. Although the communications manager 820 is illustrated as a separate component, in some examples, one or more functions described with reference to the communications manager 820 may be supported by or performed by the at least one processor 840, the at least one memory 830, the code 835, or any combination thereof. For example, the code 835 may include instructions executable by the at least one processor 840 to cause the device 805 to perform various aspects of model updates with UE latent query as described herein, or the at least one processor 840 and the at least one memory 830 may be otherwise configured to, individually or collectively, perform or support such operations.
The receiver 910 may provide a means for obtaining (e.g., receiving, determining, identifying) information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). Information may be passed on to other components of the device 905. In some examples, the receiver 910 may support obtaining information by receiving signals via one or more antennas. Additionally, or alternatively, the receiver 910 may support obtaining information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.
The transmitter 915 may provide a means for outputting (e.g., transmitting, providing, conveying, sending) information generated by other components of the device 905. For example, the transmitter 915 may output information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). In some examples, the transmitter 915 may support outputting information by transmitting signals via one or more antennas. Additionally, or alternatively, the transmitter 915 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 915 and the receiver 910 may be co-located in a transceiver, which may include or be coupled with a modem.
The communications manager 920, the receiver 910, the transmitter 915, or various combinations thereof or various components thereof may be examples of means for performing various aspects of model updates with UE latent query as described herein. For example, the communications manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be capable of performing one or more of the functions described herein.
In some examples, the communications manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry). The hardware may include at least one of a processor, a DSP, a CPU, an ASIC, an FPGA or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure. In some examples, at least one processor and at least one memory coupled with the at least one processor may be configured to perform one or more of the functions described herein (e.g., by one or more processors, individually or collectively, executing instructions stored in the at least one memory).
Additionally, or alternatively, the communications manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be implemented in code (e.g., as communications management software or firmware) executed by at least one processor. If implemented in code executed by at least one processor, the functions of the communications manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure).
In some examples, the communications manager 920 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 910, the transmitter 915, or both. For example, the communications manager 920 may receive information from the receiver 910, send information to the transmitter 915, or be integrated in combination with the receiver 910, the transmitter 915, or both to obtain information, output information, or perform various other operations as described herein.
The communications manager 920 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 920 is capable of, configured to, or operable to support a means for obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The communications manager 920 is capable of, configured to, or operable to support a means for providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
By including or configuring the communications manager 920 in accordance with examples as described herein, the device 905 (e.g., at least one processor controlling or otherwise coupled with the receiver 910, the transmitter 915, the communications manager 920, or a combination thereof) may support techniques for two-sided machine learning model training using a network-first type 3 training that includes over-the-air signaling.
The receiver 1010 may provide a means for obtaining (e.g., receiving, determining, identifying) information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). Information may be passed on to other components of the device 1005. In some examples, the receiver 1010 may support obtaining information by receiving signals via one or more antennas. Additionally, or alternatively, the receiver 1010 may support obtaining information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.
The transmitter 1015 may provide a means for outputting (e.g., transmitting, providing, conveying, sending) information generated by other components of the device 1005. For example, the transmitter 1015 may output information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). In some examples, the transmitter 1015 may support outputting information by transmitting signals via one or more antennas. Additionally, or alternatively, the transmitter 1015 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 1015 and the receiver 1010 may be co-located in a transceiver, which may include or be coupled with a modem.
The device 1005, or various components thereof, may be an example of means for performing various aspects of model updates with UE latent query as described herein. For example, the communications manager 1020 may include a first dataset manager 1025 a second dataset manager 1030, or any combination thereof. The communications manager 1020 may be an example of aspects of a communications manager 920 as described herein. In some examples, the communications manager 1020, or various components thereof, may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 1010, the transmitter 1015, or both. For example, the communications manager 1020 may receive information from the receiver 1010, send information to the transmitter 1015, or be integrated in combination with the receiver 1010, the transmitter 1015, or both to obtain information, output information, or perform various other operations as described herein.
The communications manager 1020 may support wireless communications in accordance with examples as disclosed herein. The first dataset manager 1025 is capable of, configured to, or operable to support a means for obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The second dataset manager 1030 is capable of, configured to, or operable to support a means for providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
The communications manager 1120 may support wireless communications in accordance with examples as disclosed herein. The first dataset manager 1125 is capable of, configured to, or operable to support a means for obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The second dataset manager 1130 is capable of, configured to, or operable to support a means for providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
In some examples, the training manager 1135 is capable of, configured to, or operable to support a means for inputting the first training dataset into a network-based encoder of the network-based auto-encoder, where an output of the network-based encoder provides the second training dataset.
In some examples, the network server manager 1140 is capable of, configured to, or operable to support a means for providing the first training dataset for output to a network server associated with the network entity. In some examples, the network server manager 1140 is capable of, configured to, or operable to support a means for obtaining the second training dataset from the network server in response to providing the first training dataset for output.
In some examples, the configuration manager 1145 is capable of, configured to, or operable to support a means for providing for output information associated with the network-based auto-encoder, where obtaining the indication of the first training dataset is based on the information. In some examples, the information associated with the network-based auto-encoder includes an identifier associated with the network-based auto-encoder. In some examples, the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE. In some examples, the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active.
In some examples, the first training dataset is obtained as a non-compressed dataset. In some examples, the first training dataset is obtained as a compressed dataset based on a compression algorithm. In some examples, the compression algorithm includes at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm. In some examples, the second training dataset includes an output of a network-based encoder of the network-based auto-encoder using the one or more channel metrics, the precoding metric, or both. In some examples, the configuration manager 1145 is capable of, configured to, or operable to support a means for providing for output an indication that the second training dataset is for training the UE-based encoder of the UE.
The transceiver 1210 may support bi-directional communications via wired links, wireless links, or both as described herein. In some examples, the transceiver 1210 may include a wired transceiver and may communicate bi-directionally with another wired transceiver. Additionally, or alternatively, in some examples, the transceiver 1210 may include a wireless transceiver and may communicate bi-directionally with another wireless transceiver. In some examples, the device 1205 may include one or more antennas 1215, which may be capable of transmitting or receiving wireless transmissions (e.g., concurrently). The transceiver 1210 may also include a modem to modulate signals, to provide the modulated signals for transmission (e.g., by one or more antennas 1215, by a wired transmitter), to receive modulated signals (e.g., from one or more antennas 1215, from a wired receiver), and to demodulate signals. In some implementations, the transceiver 1210 may include one or more interfaces, such as one or more interfaces coupled with the one or more antennas 1215 that are configured to support various receiving or obtaining operations, or one or more interfaces coupled with the one or more antennas 1215 that are configured to support various transmitting or outputting operations, or a combination thereof. In some implementations, the transceiver 1210 may include or be configured for coupling with one or more processors or one or more memory components that are operable to perform or support operations based on received or obtained information or signals, or to generate information or other signals for transmission or other outputting, or any combination thereof. In some implementations, the transceiver 1210, or the transceiver 1210 and the one or more antennas 1215, or the transceiver 1210 and the one or more antennas 1215 and one or more processors or one or more memory components (e.g., the at least one processor 1235, the at least one memory 1225, or both), may be included in a chip or chip assembly that is installed in the device 1205. In some examples, the transceiver 1210 may be operable to support communications via one or more communications links (e.g., a communication link 125, a backhaul communication link 120, a midhaul communication link 162, a fronthaul communication link 168).
The at least one memory 1225 may include RAM, ROM, or any combination thereof. The at least one memory 1225 may store computer-readable, computer-executable code 1230 including instructions that, when executed by one or more of the at least one processor 1235, cause the device 1205 to perform various functions described herein. The code 1230 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code 1230 may not be directly executable by a processor of the at least one processor 1235 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some cases, the at least one memory 1225 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices. In some examples, the at least one processor 1235 may include multiple processors and the at least one memory 1225 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories which may, individually or collectively, be configured to perform various functions herein (for example, as part of a processing system).
The at least one processor 1235 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA, a microcontroller, a programmable logic device, discrete gate or transistor logic, a discrete hardware component, or any combination thereof). In some cases, the at least one processor 1235 may be configured to operate a memory array using a memory controller. In some other cases, a memory controller may be integrated into one or more of the at least one processor 1235. The at least one processor 1235 may be configured to execute computer-readable instructions stored in a memory (e.g., one or more of the at least one memory 1225) to cause the device 1205 to perform various functions (e.g., functions or tasks supporting model updates with UE latent query). For example, the device 1205 or a component of the device 1205 may include at least one processor 1235 and at least one memory 1225 coupled with one or more of the at least one processor 1235, the at least one processor 1235 and the at least one memory 1225 configured to perform various functions described herein. The at least one processor 1235 may be an example of a cloud-computing platform (e.g., one or more physical nodes and supporting software such as operating systems, virtual machines, or container instances) that may host the functions (e.g., by executing code 1230) to perform the functions of the device 1205. The at least one processor 1235 may be any one or more suitable processors capable of executing scripts or instructions of one or more software programs stored in the device 1205 (such as within one or more of the at least one memory 1225). In some examples, the at least one processor 1235 may include multiple processors and the at least one memory 1225 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein. In some examples, the at least one processor 1235 may be a component of a processing system, which may refer to a system (such as a series) of machines, circuitry (including, for example, one or both of processor circuitry (which may include the at least one processor 1235) and memory circuitry (which may include the at least one memory 1225)), or components, that receives or obtains inputs and processes the inputs to produce, generate, or obtain a set of outputs. The processing system may be configured to perform one or more of the functions described herein. For example, the at least one processor 1235 or a processing system including the at least one processor 1235 may be configured to, configurable to, or operable to cause the device 1205 to perform one or more of the functions described herein. Further, as described herein, being “configured to,” being “configurable to,” and being “operable to” may be used interchangeably and may be associated with a capability, when executing code stored in the at least one memory 1225 or otherwise, to perform one or more of the functions described herein.
In some examples, a bus 1240 may support communications of (e.g., within) a protocol layer of a protocol stack. In some examples, a bus 1240 may support communications associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack), which may include communications performed within a component of the device 1205, or between different components of the device 1205 that may be co-located or located in different locations (e.g., where the device 1205 may refer to a system in which one or more of the communications manager 1220, the transceiver 1210, the at least one memory 1225, the code 1230, and the at least one processor 1235 may be located in one of the different components or divided between different components).
In some examples, the communications manager 1220 may manage aspects of communications with a core network 130 (e.g., via one or more wired or wireless backhaul links). For example, the communications manager 1220 may manage the transfer of data communications for client devices, such as one or more UEs 115. In some examples, the communications manager 1220 may manage communications with other network entities 105, and may include a controller or scheduler for controlling communications with UEs 115 in cooperation with other network entities 105. In some examples, the communications manager 1220 may support an X2 interface within an LTE/LTE-A wireless communications network technology to provide communication between network entities 105.
The communications manager 1220 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 1220 is capable of, configured to, or operable to support a means for obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The communications manager 1220 is capable of, configured to, or operable to support a means for providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
By including or configuring the communications manager 1220 in accordance with examples as described herein, the device 1205 may support techniques for two-sided machine learning model training using a network-first type 3 training that includes over-the-air signaling.
In some examples, the communications manager 1220 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the transceiver 1210, the one or more antennas 1215 (e.g., where applicable), or any combination thereof. Although the communications manager 1220 is illustrated as a separate component, in some examples, one or more functions described with reference to the communications manager 1220 may be supported by or performed by the transceiver 1210, one or more of the at least one processor 1235, one or more of the at least one memory 1225, the code 1230, or any combination thereof (for example, by a processing system including at least a portion of the at least one processor 1235, the at least one memory 1225, the code 1230, or any combination thereof). For example, the code 1230 may include instructions executable by one or more of the at least one processor 1235 to cause the device 1205 to perform various aspects of model updates with UE latent query as described herein, or the at least one processor 1235 and the at least one memory 1225 may be otherwise configured to, individually or collectively, perform or support such operations.
At 1305, the method may include transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The operations of block 1305 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1305 may be performed by a first dataset manager 725 as described with reference to
At 1310, the method may include receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. The operations of block 1310 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1310 may be performed by a second dataset manager 730 as described with reference to
At 1405, the method may include transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The operations of block 1405 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1405 may be performed by a first dataset manager 725 as described with reference to
At 1410, the method may include receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. The operations of block 1410 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1410 may be performed by a second dataset manager 730 as described with reference to
At 1415, the method may include training the UE-based encoder using the second training dataset. The operations of block 1415 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1415 may be performed by a training manager 735 as described with reference to
At 1505, the method may include transmitting an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The operations of block 1505 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1505 may be performed by a first dataset manager 725 as described with reference to
At 1510, the method may include receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. The operations of block 1510 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1510 may be performed by a second dataset manager 730 as described with reference to
At 1515, the method may include transmitting the second training dataset to a UE server associated with the UE. The operations of block 1515 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1515 may be performed by a UE server manager 740 as described with reference to
At 1520, the method may include receiving an updated encoder model for the UE-based encoder from the UE server, where the updated encoder model is based on the second training dataset. The operations of block 1520 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1520 may be performed by a UE server manager 740 as described with reference to
At 1605, the method may include obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The operations of block 1605 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1605 may be performed by a first dataset manager 1125 as described with reference to
At 1610, the method may include providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. The operations of block 1610 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1610 may be performed by a second dataset manager 1130 as described with reference to
At 1705, the method may include obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, where the first training dataset includes one or more channel metrics obtained by the UE, a precoding metric, or both. The operations of block 1705 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1705 may be performed by a first dataset manager 1125 as described with reference to
At 1710, the method may include providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, where the second training dataset is based on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and where the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder. The operations of block 1710 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1710 may be performed by a second dataset manager 1130 as described with reference to
At 1715, the method may include inputting the first training dataset into a network-based encoder of the network-based auto-encoder, where an output of the network-based encoder provides the second training dataset. The operations of block 1715 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1715 may be performed by a training manager 1135 as described with reference to
The following provides an overview of aspects of the present disclosure:
Aspect 1: A method for wireless communications at a UE, comprising: transmitting an indication of a first training dataset associated with a network-based auto-encoder, wherein the first training dataset comprises one or more channel metrics obtained by the UE, a precoding metric, or both; and receiving, in response to transmitting the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, wherein the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Aspect 2: The method of aspect 1, further comprising: training the UE-based encoder using the second training dataset.
Aspect 3: The method of any of aspects 1 through 2, further comprising: transmitting the second training dataset to a UE server associated with the UE; and receiving an updated encoder model for the UE-based encoder from the UE server, wherein the updated encoder model is based at least in part on the second training dataset.
Aspect 4: The method of any of aspects 1 through 3, further comprising: receiving information associated with the network-based auto-encoder, wherein transmitting the indication of the first training dataset is based at least in part on the information.
Aspect 5: The method of aspect 4, wherein the information associated with the network-based auto-encoder comprises an identifier associated with the network-based auto-encoder.
Aspect 6: The method of any of aspects 4 through 5, wherein the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE.
Aspect 7: The method of any of aspects 4 through 6, wherein the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active.
Aspect 8: The method of any of aspects 1 through 7, wherein the first training dataset is transmitted as a non-compressed dataset.
Aspect 9: The method of any of aspects 1 through 8, further comprising: applying a compression algorithm to the first training dataset prior to transmission.
Aspect 10: The method of aspect 9, wherein the compression algorithm comprises at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm.
Aspect 11: The method of any of aspects 1 through 10, further comprising: receiving an indication that the second training dataset is for training the UE-based encoder of the UE.
Aspect 12: A method for wireless communications at a network entity, comprising: obtaining, from a UE, an indication of a first training dataset associated with a network-based auto-encoder, wherein the first training dataset comprises one or more channel metrics obtained by the UE, a precoding metric, or both; and providing for output, in response to obtaining the indication of the first training dataset, a second training dataset associated with the network-based auto-encoder, wherein the second training dataset is based at least in part on training the network-based auto-encoder using first training inputs obtained from the first training dataset, and wherein the second training dataset is for training a UE-based encoder that corresponds to the network-based auto-encoder.
Aspect 13: The method of aspect 12, further comprising: inputting the first training dataset into a network-based encoder of the network-based auto-encoder, wherein an output of the network-based encoder provides the second training dataset.
Aspect 14: The method of any of aspects 12 through 13, further comprising: providing the first training dataset for output to a network server associated with the network entity; and obtaining the second training dataset from the network server in response to providing the first training dataset for output.
Aspect 15: The method of any of aspects 12 through 14, further comprising: providing for output information associated with the network-based auto-encoder, wherein obtaining the indication of the first training dataset is based at least in part on the information.
Aspect 16: The method of aspect 15, wherein the information associated with the network-based auto-encoder comprises an identifier associated with the network-based auto-encoder.
Aspect 17: The method of any of aspects 15 through 16, wherein the information associated with the network-based auto-encoder indicates a set of communication parameters associated with the one or more channel metrics obtained by the UE.
Aspect 18: The method of any of aspects 15 through 17, wherein the information associated with the network-based auto-encoder indicates at least one of a time period or a location in which the network-based auto-encoder is active.
Aspect 19: The method of any of aspects 12 through 18, wherein the first training dataset is obtained as a non-compressed dataset.
Aspect 20: The method of any of aspects 12 through 18, wherein the first training dataset is obtained as a compressed dataset based at least in part on a compression algorithm.
Aspect 21: The method of aspect 20, wherein the compression algorithm comprises at least one of a machine-learning-based algorithm or a non-machine-learning-based algorithm.
Aspect 22: The method of any of aspects 12 through 21, further comprising: providing for output an indication that the second training dataset is for training the UE-based encoder of the UE.
Aspect 23: A UE for wireless communications, comprising one or more memories storing processor-executable code, and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to perform a method of any of aspects 1 through 11.
Aspect 24: A UE for wireless communications, comprising at least one means for performing a method of any of aspects 1 through 11.
Aspect 25: A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to perform a method of any of aspects 1 through 11.
Aspect 26: A network entity for wireless communications, comprising one or more memories storing processor-executable code, and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the network entity to perform a method of any of aspects 12 through 22.
Aspect 27: A network entity for wireless communications, comprising at least one means for performing a method of any of aspects 12 through 22.
Aspect 28: A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to perform a method of any of aspects 12 through 22.
It should be noted that the methods described herein describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Further, aspects from two or more of the methods may be combined.
Although aspects of an LTE, LTE-A, LTE-A Pro, or NR system may be described for purposes of example, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein are applicable beyond LTE, LTE-A, LTE-A Pro, or NR networks. For example, the described techniques may be applicable to various other wireless communications systems such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, as well as other systems and radio technologies not explicitly mentioned herein.
Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
The various illustrative blocks and components described in connection with the disclosure herein may be implemented or performed using a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor but, in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration). Any functions or operations described herein as being capable of being performed by a processor may be performed by multiple processors that, individually or collectively, are capable of performing the described functions or operations.
The functions described herein may be implemented using hardware, software executed by a processor, firmware, or any combination thereof. If implemented using software executed by a processor, the functions may be stored as or transmitted using one or more instructions or code of a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of computer-readable medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc. Disks may reproduce data magnetically, and discs may reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media. Any functions or operations described herein as being capable of being performed by a memory may be performed by multiple memories that, individually or collectively, are capable of performing the described functions or operations.
As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of”) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on.”
As used herein, including in the claims, the article “a” before a noun is open-ended and understood to refer to “at least one” of those nouns or “one or more” of those nouns. Thus, the terms “a,” “at least one,” “one or more,” “at least one of one or more” may be interchangeable. For example, if a claim recites “a component” that performs one or more functions, each of the individual functions may be performed by a single component or by any combination of multiple components. Thus, the term “a component” having characteristics or performing functions may refer to “at least one of one or more components” having a particular characteristic or performing a particular function. Subsequent reference to a component introduced with the article “a” using the terms “the” or “said” may refer to any or all of the one or more components. For example, a component introduced with the article “a” may be understood to mean “one or more components,” and referring to “the component” subsequently in the claims may be understood to be equivalent to referring to “at least one of the one or more components.” Similarly, subsequent reference to a component introduced as “one or more components” using the terms “the” or “said” may refer to any or all of the one or more components. For example, referring to “the one or more components” subsequently in the claims may be understood to be equivalent to referring to “at least one of the one or more components.”
The term “determine” or “determining” encompasses a variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, a database, or another data structure), ascertaining and the like. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data stored in memory) and the like. Also, “determining” can include resolving, obtaining, selecting, choosing, establishing, and other such similar actions.
In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If just the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label, or other subsequent reference label.
The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “example” used herein means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.
The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.
