The present disclosure relates generally to communication systems, and more particularly, to wireless communication including machine learning.
Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.
These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. An example telecommunication standard is 5G New Radio (NR). 5G NR is part of a continuous mobile broadband evolution promulgated by Third Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with Internet of Things (IoT)), and other requirements. 5G NR includes services associated with enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communications (URLLC). Some aspects of 5G NR may be based on the 4G Long Term Evolution (LTE) standard. There exists a need for further improvements in 5G NR technology. These improvements may also be applicable to other multi-access technologies and the telecommunication standards that employ these technologies.
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects. This summary neither identifies key or critical elements of all aspects nor delineates the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus processes information with machine learning associated with a model identifier (ID), a machine learning function, or a machine learning use case; and reports data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case.
In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus provides a configuration for machine learning associated with a model ID, a machine learning function or, a machine learning use case; and receives a report of data based on the configuration associated with the model ID, the machine learning function, or the machine learning use case.
To the accomplishment of the foregoing and related ends, the one or more aspects comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative, however, of but a few of the various ways in which the principles of various aspects may be employed.
Performance of an artificial intelligence (AI) or machine learning (ML) model may depend on the quality of the datasets used in connection with the AI/ML model. As an example, a large number of unnecessary feature spaces (which may be referred to as input parameters) may result in overfitting. For example, overfitting may refer to model predictions or inferences on field data that lose accuracy, e.g., low accuracy due to biases towards training data, in order to fit the training input data. The selection of the feature space (e.g., the input parameter set) may help to avoid overfitting. Principle component analysis (PCA) techniques may be used to select the feature space (e.g., input parameters set) to improve AI/ML performance by avoiding overfitting.
Similarly, an imbalanced dataset may negatively impact the AI/ML model performance. For example, if there is a greater number of observations for one class than for another, then the AI/ML model may tend to overfit towards the class having more observations. In order to avoid having unbalanced observations in the dataset, the data may be independently and identically distributed (i.i.d.).
Aspects presented herein provide for AI/ML data collection, reporting, and/or validation aspects that can improve the AI/ML performance. Aspects presented herein provide for the data collection and reporting (including the processing of the data for inference or training) to be configured and requested per AI/ML model identifier (ID), per AI/ML use case, or per AI/ML function. Aspects presented herein further provide for data validation to determine if collected data meets criteria to be included for training and inference based on the AI/ML model. The aspects presented herein may help to reduce the use of over-the-air resources by processing and reporting validated data and skipping the processing and/or reporting of data that does not meet validation criteria. Aspects presented herein may also help to reduce inference and online-training delay by reducing a data processing delay.
The detailed description set forth below in connection with the drawings describes various configurations and does not represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts.
Several aspects of telecommunication systems are presented with reference to various apparatus and methods. These apparatus and methods are described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “elements”). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.
By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system may execute software. Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination thereof.
Accordingly, in one or more example aspects, implementations, and/or use cases, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, such computer-readable media can comprise a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the types of computer-readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.
While aspects, implementations, and/or use cases are described in this application by illustration to some examples, additional or different aspects, implementations and/or use cases may come about in many different arrangements and scenarios. Aspects, implementations, and/or use cases described herein may be implemented across many differing platform types, devices, systems, shapes, sizes, and packaging arrangements. For example, aspects, implementations, and/or use cases may come about via integrated chip implementations and other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, artificial intelligence (AI)-enabled devices, etc.). While some examples may or may not be specifically directed to use cases or applications, a wide assortment of applicability of described examples may occur. Aspects, implementations, and/or use cases may range a spectrum from chip-level or modular components to non-modular, non-chip-level implementations and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more techniques herein. In some practical settings, devices incorporating described aspects and features may also include additional components and features for implementation and practice of claimed and described aspect. For example, transmission and reception of wireless signals necessarily includes a number of components for analog and digital purposes (e.g., hardware components including antenna, RF-chains, power amplifiers, modulators, buffer, processor(s), interleaver, adders/summers, etc.). Techniques described herein may be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc. of varying sizes, shapes, and constitution.
Deployment of communication systems, such as 5G NR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element, or a network equipment, such as a base station (BS), or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a BS (such as a Node B (NB), evolved NB (eNB), NR BS, 5G NB, access point (AP), a transmit receive point (TRP), or a cell, etc.) may be implemented as an aggregated base station (also known as a standalone BS or a monolithic BS) or a disaggregated base station.
An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU and RU can be implemented as virtual units, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).
Base station operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as the network configuration sponsored by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.
Each of the units, i.e., the CUs 110, the DUs 130, the RUs 140, as well as the Near-RT RICs 125, the Non-RT RICs 115, and the SMO Framework 105, may include one or more interfaces or be coupled to one or more interfaces configured to receive or to transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or to transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter, or a transceiver (such as an RF transceiver), configured to receive or to transmit signals, or both, over a wireless transmission medium to one or more of the other units.
In some aspects, the CU 110 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 110. The CU 110 may be configured to handle user plane functionality (i.e., Central Unit-User Plane (CU-UP)), control plane functionality (i.e., Central Unit-Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 110 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as an E1 interface when implemented in an O-RAN configuration. The CU 110 can be implemented to communicate with the DU 130, as necessary, for network control and signaling.
The DU 130 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 140. In some aspects, the DU 130 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, demodulation, or the like) depending, at least in part, on a functional split, such as those defined by 3GPP. In some aspects, the DU 130 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 130, or with the control functions hosted by the CU 110.
Lower-layer functionality can be implemented by one or more RUs 140. In some deployments, an RU 140, controlled by a DU 130, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 140 can be implemented to handle over the air (OTA) communication with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU(s) 140 can be controlled by the corresponding DU 130. In some scenarios, this configuration can enable the DU(s) 130 and the CU 110 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.
The SMO Framework 105 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 105 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements that may be managed via an operations and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO Framework 105 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 190) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements can include, but are not limited to, CUs 110, DUs 130, RUs 140 and Near-RT RICs 125. In some implementations, the SMO Framework 105 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 111, via an O1 interface. Additionally, in some implementations, the SMO Framework 105 can communicate directly with one or more RUs 140 via an O1 interface. The SMO Framework 105 also may include a Non-RT RIC 115 configured to support functionality of the SMO Framework 105.
The Non-RT RIC 115 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence (AI)/machine learning (ML) (AI/ML) workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 125. The Non-RT RIC 115 may be coupled to or communicate with (such as via an A1 interface) the Near-RT RIC 125. The Near-RT RIC 125 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 110, one or more DUs 130, or both, as well as an O-eNB, with the Near-RT RIC 125.
In some implementations, to generate AI/ML models to be deployed in the Near-RT RIC 125, the Non-RT RIC 115 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 125 and may be received at the SMO Framework 105 or the Non-RT RIC 115 from non-network data sources or from network functions. In some examples, the Non-RT RIC 115 or the Near-RT RIC 125 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 115 may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions through the SMO Framework 105 (such as reconfiguration via 01) or via creation of RAN management policies (such as A1 policies).
At least one of the CU 110, the DU 130, and the RU 140 may be referred to as a base station 102. Accordingly, a base station 102 may include one or more of the CU 110, the DU 130, and the RU 140 (each component indicated with dotted lines to signify that each component may or may not be included in the base station 102). The base station 102 provides an access point to the core network 120 for a UE 104. The base stations 102 may include macrocells (high power cellular base station) and/or small cells (low power cellular base station). The small cells include femtocells, picocells, and microcells. A network that includes both small cell and macrocells may be known as a heterogeneous network. A heterogeneous network may also include Home Evolved Node Bs (eNBs) (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG). The communication links between the RUs 140 and the UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to an RU 140 and/or downlink (DL) (also referred to as forward link) transmissions from an RU 140 to a UE 104. The communication links may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links may be through one or more carriers. The base stations 102/UEs 104 may use spectrum up to Y MHz (e.g., 5, 10, 15, 20, 100, 400, etc. MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x component carriers) used for transmission in each direction. The carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL than for UL). The component carriers may include a primary component carrier and one or more secondary component carriers. A primary component carrier may be referred to as a primary cell (PCell) and a secondary component carrier may be referred to as a secondary cell (SCell).
Certain UEs 104 may communicate with each other using device-to-device (D2D) communication link 158. The D2D communication link 158 may use the DL/UL wireless wide area network (WWAN) spectrum. The D2D communication link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), and a physical sidelink control channel (PSCCH). D2D communication may be through a variety of wireless D2D communications systems, such as for example, Bluetooth, Wi-Fi based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.
The wireless communications system may further include a Wi-Fi AP 150 in communication with UEs 104 (also referred to as Wi-Fi stations (STAs)) via communication link 154, e.g., in a 5 GHz unlicensed frequency spectrum or the like. When communicating in an unlicensed frequency spectrum, the UEs 104/AP 150 may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.
The electromagnetic spectrum is often subdivided, based on frequency/wavelength, into various classes, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz-300 GHz) which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band.
The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz-24.25 GHz). Frequency bands falling within FR3 may inherit FR1 characteristics and/or FR2 characteristics, and thus may effectively extend features of FR1 and/or FR2 into mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR2-2 (52.6 GHz-71 GHz), FR4 (71 GHz-114.25 GHz), and FR5 (114.25 GHz-300 GHz). Each of these higher frequency bands falls within the EHF band.
With the above aspects in mind, unless specifically stated otherwise, the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Further, unless specifically stated otherwise, the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR2-2, and/or FR5, or may be within the EHF band.
The base station 102 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and/or antenna arrays to facilitate beamforming. The base station 102 may transmit a beamformed signal 182 to the UE 104 in one or more transmit directions. The UE 104 may receive the beamformed signal from the base station 102 in one or more receive directions. The UE 104 may also transmit a beamformed signal 184 to the base station 102 in one or more transmit directions. The base station 102 may receive the beamformed signal from the UE 104 in one or more receive directions. The base station 102/UE 104 may perform beam training to determine the best receive and transmit directions for each of the base station 102/UE 104. The transmit and receive directions for the base station 102 may or may not be the same. The transmit and receive directions for the UE 104 may or may not be the same.
The base station 102 may include and/or be referred to as a gNB, Node B, eNB, an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a transmit reception point (TRP), network node, network entity, network equipment, or some other suitable terminology. The base station 102 can be implemented as an integrated access and backhaul (IAB) node, a relay node, a sidelink node, an aggregated (monolithic) base station with a baseband unit (BBU) (including a CU and a DU) and an RU, or as a disaggregated base station including one or more of a CU, a DU, and/or an RU. The set of base stations, which may include disaggregated base stations and/or aggregated base stations, may be referred to as next generation (NG) RAN (NG-RAN).
The core network 120 may include an Access and Mobility Management Function (AMF) 161, a Session Management Function (SMF) 162, a User Plane Function (UPF) 163, a Unified Data Management (UDM) 164, one or more location servers, and other functional entities. The AMF 161 is the control node that processes the signaling between the UEs 104 and the core network 120. The AMF 161 supports registration management, connection management, mobility management, and other functions. The SMF 162 supports session management and other functions. The UPF 163 supports packet routing, packet forwarding, and other functions. The UDM 164 supports the generation of authentication and key agreement (AKA) credentials, user identification handling, access authorization, and subscription management. The one or more location servers are illustrated as including a Gateway Mobile Location Center (GMLC) 165 and a Location Management Function (LMF) 166. However, generally, the one or more location servers may include one or more location/positioning servers, which may include one or more of the GMLC 165, the LMF 166, a position determination entity (PDE), a serving mobile location center (SMLC), a mobile positioning center (MPC), or the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients/applications (e.g., emergency services) for accessing UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute the position of the UE 104. The NG-RAN may utilize one or more positioning methods in order to determine the position of the UE 104. Positioning the UE 104 may involve signal measurements, a position estimate, and an optional velocity computation based on the measurements. The signal measurements may be made by the UE 104 and/or the serving base station 102. The signals measured may be based on one or more of a satellite positioning system (SPS) 170 (e.g., one or more of a Global Navigation Satellite System (GNSS), global position system (GPS), non-terrestrial network (NTN), or other satellite position/location system), LTE signals, wireless local area network (WLAN) signals, Bluetooth signals, a terrestrial beacon system (TBS), sensor-based information (e.g., barometric pressure sensor, motion sensor), NR enhanced cell ID (NR E-CID) methods, NR signals (e.g., multi-round trip time (Multi-RTT), DL angle-of-departure (DL-AoD), DL time difference of arrival (DL-TDOA), UL time difference of arrival (UL-TDOA), and UL angle-of-arrival (UL-AoA) positioning), and/or other systems/signals/sensors.
Examples of UEs 104 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor/actuator, a display, or any other similar functioning device. Some of the UEs 104 may be referred to as IoT devices (e.g., parking meter, gas pump, toaster, vehicles, heart monitor, etc.). The UE 104 may also be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. In some scenarios, the term UE may also apply to one or more companion devices such as in a device constellation arrangement. One or more of these devices may collectively access the network and/or individually access the network.
Referring again to
Although the following description may be focused on 5G NR, the concepts described herein may be applicable to other similar areas, such as LTE, LTE-A, CDMA, GSM, and other wireless technologies.
For normal CP (14 symbols/slot), different numerologies μ 0 to 4 allow for 1, 2, 4, 8, and 16 slots, respectively, per subframe. For extended CP, the numerology 2 allows for 4 slots per subframe. Accordingly, for normal CP and numerology μ, there are 14 symbols/slot and 2μ slots/subframe. The subcarrier spacing may be equal to 2μ*15 kHz, where μ is the numerology 0 to 4. As such, the numerology μ=0 has a subcarrier spacing of 15 kHz and the numerology μ=4 has a subcarrier spacing of 240 kHz. The symbol length/duration is inversely related to the subcarrier spacing.
A resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as physical RBs (PRBs)) that extends 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.
As illustrated in
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The transmit (TX) processor 316 and the receive (RX) processor 370 implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding/decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation/demodulation of physical channels, and MIMO antenna processing. The TX processor 316 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and/or frequency domain, and then combined together using an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator 374 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the UE 350. Each spatial stream may then be provided to a different antenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx may modulate a radio frequency (RF) carrier with a respective spatial stream for transmission.
At the UE 350, each receiver 354Rx receives a signal through its respective antenna 352. Each receiver 354Rx recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 356. The TX processor 368 and the RX processor 356 implement layer 1 functionality associated with various signal processing functions. The RX processor 356 may perform spatial processing on the information to recover any spatial streams destined for the UE 350. If multiple spatial streams are destined for the UE 350, they may be combined by the RX processor 356 into a single OFDM symbol stream. The RX processor 356 then converts the OFDM symbol stream from the time-domain to the frequency domain using a Fast Fourier Transform (FFT). The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 310. These soft decisions may be based on channel estimates computed by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the base station 310 on the physical channel. The data and control signals are then provided to the controller/processor 359, which implements layer 3 and layer 2 functionality.
The controller/processor 359 can be associated with a memory 360 that stores program codes and data. The memory 360 may be referred to as a computer-readable medium. In the UL, the controller/processor 359 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets. The controller/processor 359 is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations.
Similar to the functionality described in connection with the DL transmission by the base station 310, the controller/processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIB s) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression/decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.
Channel estimates derived by a channel estimator 358 from a reference signal or feedback transmitted by the base station 310 may be used by the TX processor 368 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor 368 may be provided to different antenna 352 via separate transmitters 354Tx. Each transmitter 354Tx may modulate an RF carrier with a respective spatial stream for transmission.
The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver function at the UE 350. Each receiver 318Rx receives a signal through its respective antenna 320. Each receiver 318Rx recovers information modulated onto an RF carrier and provides the information to a RX processor 370.
The controller/processor 375 can be associated with a memory 376 that stores program codes and data. The memory 376 may be referred to as a computer-readable medium. In the UL, the controller/processor 375 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets. The controller/processor 375 is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations.
At least one of the TX processor 368, the RX processor 356, and the controller/processor 359 may be configured to perform aspects in connection with the machine learning component 198 and/or the machine learning configuration component 199 of
At least one of the TX processor 316, the RX processor 370, and the controller/processor 375 may be configured to perform aspects in connection with the machine learning component 198 and/or the machine learning configuration component 199 of
The data collection 402 may be a function that provides input data to the model training function 404 and the model inference function 406. The data collection 402 function may include any form of data preparation, and it may not be specific to the implementation of the AI/ML algorithm (e.g., data pre-processing and cleaning, formatting, and transformation). The examples of input data may include, but not limited to, radio measurements, such as a reference signal received power (RSRP) for a cell and/or a beam, from UEs or network nodes, feedback from the actor 408, output from another AI/ML model. As an example, the measurements may be of a reference signal such as a CSI-RS, or a precoding metric, among other examples. The data collection 402 may include training data, which refers to the data to be sent as the input for the AI/ML model training function 404, and inference data, which refers to be sent as the input for the AI/ML model inference function 406.
The model training function 404 may be a function that performs the ML model training, validation, and testing, which may generate model performance metrics as part of the model testing procedure. The model training function 404 may also be responsible for data preparation (e.g. data pre-processing and cleaning, formatting, and transformation) based on the training data delivered or received from the data collection 402 function. The model training function 404 may deploy or update a trained, validated, and tested AI/ML model to the model inference function 406, and receive a model performance feedback from the model inference function 406.
The model inference function 406 may be a function that provides the AI/ML model inference output (e.g. predictions or decisions). The model inference function 406 may also perform data preparation (e.g. data pre-processing and cleaning, formatting, and transformation) based on the inference data delivered from the data collection 402 function. The output of the model inference function 406 may include the inference output of the AI/ML model produced by the model inference function 406. The details of the inference output may be use-case specific. As an example, the output may include a predicted beam (e.g., for a beam measurement use case), a predicted CSI-RS RSRP and/or precoding metric (e.g., for a CSI feedback use case), a predicted UE position (e.g., for a positioning use case), among other examples. In some aspects, the actor may be a UE. In some aspects, the actor may be a network node. In some aspects, the UE may report the output to a network node or to another UE. In some aspects, the actor may be a network node. The network node may report the output to a UE or to another network node.
The model performance feedback may refer to information derived from the model inference function 406 that may be suitable for improvement of the AI/ML model trained in the model training function 404. The feedback from the actor 408 or other network entities (via the data collection 402 function) may be implemented for the model inference function 406 to create the model performance feedback.
The actor 408 may be a function that receives the output from the model inference function 406 and triggers or performs corresponding actions. The actor may trigger actions directed to network entities including the other network entities or itself. The actor 408 may also provide a feedback information that the model training function 404 or the model interference function 406 to derive training or inference data or performance feedback. The feedback may be transmitted back to the data collection 402.
The network may use machine-learning algorithms, deep-learning algorithms, neural networks, reinforcement learning, regression, boosting, or advanced signal processing methods for aspects of wireless communication including the identification of neighbor TCI candidates for autonomous TCI candidate set updates based on DCI selection of a TCI state.
In some aspects described herein, the network may train one or more neural networks to learn dependence of measured qualities on individual parameters. Among others, examples of machine learning models or neural networks that may be comprised in the network entity include artificial neural networks (ANN); decision tree learning; convolutional neural networks (CNNs); deep learning architectures in which an output of a first layer of neurons becomes an input to a second layer of neurons, and so forth; support vector machines (SVM), e.g., including a separating hyperplane (e.g., decision boundary) that categorizes data; regression analysis; bayesian networks; genetic algorithms; Deep convolutional networks (DCNs) configured with additional pooling and normalization layers; and Deep belief networks (DBNs).
A machine learning model, such as an artificial neural network (ANN), may include an interconnected group of artificial neurons (e.g., neuron models), and may be a computational device or may represent a method to be performed by a computational device. The connections of the neuron models may be modeled as weights. Machine learning models may provide predictive modeling, adaptive control, and other applications through training via a dataset. The model may be adaptive based on external or internal information that is processed by the machine learning model. Machine learning may provide non-linear statistical data model or decision making and may model complex relationships between input data and output information.
A machine learning model may include multiple layers and/or operations that may be formed by concatenation of one or more of the referenced operations. Examples of operations that may be involved include extraction of various features of data, convolution operations, fully connected operations that may be activated or deactivates, compression, decompression, quantization, flattening, etc. As used herein, a “layer” of a machine learning model may be used to denote an operation on input data. For example, a convolution layer, a fully connected layer, and/or the like may be used to refer to associated operations on data that is input into a layer. A convolution A×B operation refers to an operation that converts a number of input features A into a number of output features B. “Kernel size” may refer to a number of adjacent coefficients that are combined in a dimension. As used herein, “weight” may be used to denote one or more coefficients used in the operations in the layers for combining various rows and/or columns of input data. For example, a fully connected layer operation may have an output y that is determined based at least in part on a sum of a product of input matrix x and weights A (which may be a matrix) and bias values B (which may be a matrix). The term “weights” may be used herein to generically refer to both weights and bias values. Weights and biases are examples of parameters of a trained machine learning model. Different layers of a machine learning model may be trained separately.
Machine learning models may include a variety of connectivity patterns, e.g., including any of feed-forward networks, hierarchical layers, recurrent architectures, feedback connections, etc. The connections between layers of a neural network may be fully connected or locally connected. In a fully connected network, a neuron in a first layer may communicate its output to each neuron in a second layer, and each neuron in the second layer may receive input from every neuron in the first layer. In a locally connected network, a neuron in a first layer may be connected to a limited number of neurons in the second layer. In some aspects, a convolutional network may be locally connected and configured with shared connection strengths associated with the inputs for each neuron in the second layer. A locally connected layer of a network may be configured such that each neuron in a layer has the same, or similar, connectivity pattern, but with different connection strengths.
A machine learning model or neural network may be trained. For example, a machine learning model may be trained based on supervised learning. During training, the machine learning model may be presented with input that the model uses to compute to produce an output. The actual output may be compared to a target output, and the difference may be used to adjust parameters (such as weights and biases) of the machine learning model in order to provide an output closer to the target output. Before training, the output may be incorrect or less accurate, and an error, or difference, may be calculated between the actual output and the target output. The weights of the machine learning model may then be adjusted so that the output is more closely aligned with the target. To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted slightly. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted so as to reduce the error or to move the output closer to the target. This manner of adjusting the weights may be referred to as back propagation through the neural network. The process may continue until an achievable error rate stops decreasing or until the error rate has reached a target level.
The machine learning models may include computational complexity and substantial processor for training the machine learning model. An output of one node is connected as the input to another node. Connections between nodes may be referred to as edges, and weights may be applied to the connections/edges to adjust the output from one node that is applied as input to another node. Nodes may apply thresholds in order to determine whether, or when, to provide output to a connected node. The output of each node may be calculated as a non-linear function of a sum of the inputs to the node. The neural network may include any number of nodes and any type of connections between nodes. The neural network may include one or more hidden nodes. Nodes may be aggregated into layers, and different layers of the neural network may perform different kinds of transformations on the input. A signal may travel from input at a first layer through the multiple layers of the neural network to output at a last layer of the neural network and may traverse layers multiple times.
Performance of an AI or ML model performance may depend on the quality of the datasets used in connection with the AI/ML model.
Similarly, an imbalanced dataset may negatively impact the AI/ML model performance. For example, if there is a greater number of observations for one class than for another, then the AI/ML model may tend to overfit towards the class having more observations. In order to avoid having unbalanced observations in the dataset, the data may be independently and identically distributed (i.i.d.).
Aspects presented herein provide for AI/ML data collection, reporting, and/or validation aspects that can improve the AI/ML performance. Aspects presented herein provide for the data collection and reporting (including the processing of the data for inference or training) to be configured, e.g., configured for a UE by a network node or another UE, requested by UE from a network node or another UE, configured for a network node by another network node, among other examples. Aspects presented herein further provide for data validation to determine if collected data meets criteria to be included for training and inference based on the AI/ML model. The aspects presented herein may help to reduce the use of over-the-air resources by processing and reporting validated data and skipping the processing and/or reporting of data that does not meet validation criteria. Aspects presented herein may also help to reduce inference and online-training delay by reducing a data processing delay.
There are various AI/ML inference and/or training use cases and related data processing procedures for application in a wireless network. As a first example, the AI/ML training and inference may be performed at a UE using UE data. In such an example, a model designer (e.g., a UE or a UE vendor) may determine a most significant feature space (e.g., the input parameter set), which may use PCA. The data for training and inference may then be collected at the UE, e.g., as described in connection with the example in
In another example, the training and inference may be performed at the UE using UE data and network data. In such an example, a model designer (e.g., a UE or a UE vendor) may determine a most significant feature space (e.g., the input parameter set), which may use PCA. In some aspects, the UE may indicate to the network the data to be collected at the network. The data for training and/or inference may then be collected at the network and at the UE. The UE and the network may perform data validation, e.g., determining whether the data is balanced.
As another example, the AI/ML training and inference may be performed at a network node using network data. In such an example, a model designer (e.g., a network entity or network vendor) may determine a most significant feature space (e.g., the input parameter set), which may use PCA. The data for training and inference may then be collected at the network node, e.g., as described in connection with the example in
In another example, the training and inference may be performed at a network node using UE data and network data. In such an example, a model designer (e.g., a network entity or network vendor) may determine a most significant feature space (e.g., the input parameter set), which may use PCA. In some aspects, the network node may indicate to the UE the data to be collected at the UE. The data for training and/or inference may then be collected at the network and at the UE. The UE and the network may perform data validation, e.g., determining whether the data is balanced.
In another example, the training an interference may be performed at a UE, e.g., as configured by a network node. In such an example, a model designer (e.g., a network entity or network vendor) may determine a most significant feature space (e.g., the input parameter set), which may use PCA. The network node may configure the UE to perform the data training and/or inference based on the AI/ML model. The network node may provide a scheme for data validation to the UE. The UE performs data validation for training and/or inference according to the data validation scheme provided by the network node. In some aspects, the UE may perform training when the data validation succeeds and may skip training the AI/ML model if the data does meet the validation criteria according to the validation scheme provided by the network node.
A UE may be configured by a network node with different measurement objects and identities are used for configuring the measurements.
Aspects presented herein provide for AI/ML based data collection, reporting, and validation by one or more devices in a wireless network. Aspects enable the input features for inference and online-training to be reported in a timely fashion and may help to minimize a processing delay associated with obtaining input features from the raw data (e.g., data observed at a UE and/or network node). Raw data may also be referred to as source data, atomic data or primary data. Raw data is data that has not been processed for use. The raw data is the data collected at the network or the UE and from which the input feature of the model may be derived. Aspects presented herein provide for the validation of input features before such input features are provided as input to inference and/or training engines. Aspects may also provide for the validation of output data before such data is reported to a network or a UE.
A large number of parameters may be configured at 606 for the measurements to be performed by the UE 602. Different AI/ML models, AI/ML function, and/or use cases may use different subsets of the configured measurements, such that at least a subset of the configured measurements may not be relevant for a particular AI/ML model, AI/ML function, or use case. For different AI/ML models, the input features (e.g., input parameters) may involve different conditions and periodicity for reporting. Aspects presented herein enable different measurement collection and/or reporting configurations to be provided with different conditions for measurement collection and/or reporting for different AI/ML models, AI/ML functions, and/or use cases. The measurements may be processed differently for different AI/ML models, AI/ML functions, or use cases. For example, normalization of measurements, filtering, and other validation of measurements may be used in connection with one or more AI/ML models, AI/ML function, or use cases, as the data validation may affect the AI/ML performance.
In each of
As illustrated at 1010, the second device 1002 may transmit a data reporting configuration to the first device 1004. The second device 1002 may be a base station, a component of a base station, or may implement base station functionality or may be a UE or UE vendor, e.g., based on the particular training and inference scenario. Various examples of training and inference scenarios are described in connection with
In some aspects, at 1012, the second device 1002 may activate or deactivate an AI/ML model, use case, of function that was configured at 1010. As an example, a network node (e.g., as the second device 1002) may transmit a MAC-CE or DCI to a UE (e.g., as the first device 1004) at 1012, that activates or deactivates data reporting for one or more AI/ML model IDs, use cases, or functions that were configured, at 1010. As another example, a UE (e.g., as the second device 1002) may transmit a MAC-CE or DCI to a network node or to another UE (e.g., as the first device 1004) at 1012, that activates or deactivates data reporting for one or more AI/ML model IDs, use cases, or functions that were configured, at 1010.
At 1014, the first device 1004 reports the data according to the configured (e.g., and activated) AI/ML model ID, use case, or function. The first device 1004 may transmit the data report in any combination of an RRC message, a MAC-CE, UCI, and/or DCI to the second device 1002.
At 1106, the second device 1002 transmits a data validation configuration to the first device 1004. For example, the data validation configuration may be provided to the first device 1004 in association with a data reporting configuration, e.g., such as 1010. In the data validation configuration, the configuring or requesting device (whether a UE or network node) can provide the rules or criteria for data validation. As an example, the second device 1002 may indicate a set of data statistics and/or properties that the AI/ML inference and training data are to follow for a particular model ID, use case, or AI/ML function. For example, the new data that is observed or measured by the first device 1004 may be checked for errors by comparing the newly observed data, at 1107, against the set of predefined data properties, statistics, criteria, or rules in the data validation configuration for the corresponding model ID, use case, or function. The data validation configuration may be provided to the second device 1002 per model ID/use case/or AI/ML function and may be provided together with a data collection and reporting configuration for the corresponding model ID, use case, or function, e.g., as described in connection with
As illustrated at 1108, the device 1104 may provide a data validation failure indication if the observed data does not meet the configured validation criteria. Otherwise, the device may report the data, e.g., as illustrated in any of
In some aspects, if the data indication fails, then the network node and/or the UE may fallback to a different procedure, such as a non-AI/ML measurement and reporting procedure. In some aspects, the validation failure indication, at 1108 may indicate the that device 1104 will switch to a different reporting procedure and/or may indicate to the device 1102 to use a non-AI/ML procedure for wireless communication with the device 1104. In some aspects, the reported data may be for federated learning. In such aspects, the validation failure indication 1108 may indicate to the device 1102 that the device 1104 did not obtain the weight for a particular epoch.
As an example, a network may request or indicate to a UE the data to be collected, processed, and/or reported per model ID, per use case, or per AI/ML function. As an example, for AI/ML training, federated learning, or inference at a network node, the network node may indicate the data processing modules/techniques to be used.
Similarly, a UE may request or indicate to a network node or another UE the data to be collected, processed, and/or reported per model ID, per use case, or per AI/ML function. The UE may request the training or inference data per model ID, use case, or AI/ML function from the network using UAI, in some aspects. In some aspects, the UE may indicate feature spaces/parameters for the network node or other UE to reported to the requesting UE. In some aspects, the UE may indicate a set of raw data from which the input features/parameters are to be obtained for the AI/ML model. In some aspects, the UE may indicate data and processing modules/techniques, e.g., 1204a or 1204b, to be used for the data collection and/or reporting. In some aspects, the UE may indicate a configuration for training and/or inference data validation, at 1210, to be performed before data is reported to the UE.
As described in connection with
For inference, federated learning, or training (online and offline training) at the network, e.g., as in
From a perspective of the network node, the network may provide a configuration for the data collection, reporting, and validation per model ID/use case/ML function, as described in connection with any of
As described in connection with any of
At 1306, the UE 1302 may receive configurations for multiple AI/ML models, such as AI/ML model 1, AI/ML model 2, AI/ML model 3, and AI/ML model 4. The UE 1302 may receive the configurations separately or together. At 1308, the base station 1304 may transmit a MAC-CE, or a DCI, that activates the AI/ML model 1. At 1310, the UE 1302 may collect data for training, inference, and/or reporting, based on AI/ML model 1. The UE may also validate the data using criteria from the configuration, at 1306, such as described in connection with
At 1316, the base station 1304 may transmit a MAC-CE, or a DCI, that activates the AI/ML model 2. In some aspects, the base station may deactivate the AI/ML model 1, and the UE may cease the training/collection/validation/reporting based on the model 1. In some aspects, the activation of the model 2 may indicate a deactivation of the model 1. In other aspects, the UE may continue to collect and report data based on the model 1 and may also collect and report data based on the model 2 in response to the activation. At 1318, the UE 1302 may collect data for training, inference, and/or reporting, based on AI/ML model 2. The UE may also validate the data using criteria from the configuration, at 1306, such as described in connection with
At 1406, the device processes information with machine learning associated with a model ID, a machine learning function, or a machine learning use case. The processing may be performed, e.g., by the machine learning component 198. The processing may include any of the aspects described in connection with
At 1410, the device reports data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case. The reporting may be performed, e.g., by the machine learning component 198. The reporting may include any of the aspects described in connection with any of
At 1406, the device processes information with machine learning associated with a model ID, a machine learning function, or a machine learning use case. The processing may be performed, e.g., by the machine learning component 198. The processing may include any of the aspects described in connection with
At 1410, the device reports data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case. The reporting may be performed, e.g., by the machine learning component 198. The reporting may include any of the aspects described in connection with any of
As illustrated at 1402, the device may receive the configuration identifying the model ID, the machine learning function, or the machine learning use case, where reporting the data includes transmitting the data based on a condition, timing, or periodicity indicated in the configuration for the model ID, the machine learning function, or the machine learning use case. The reception may be performed, e.g., by the machine learning component 198. The configuration associated with the model ID, the machine learning function, or the machine learning use case may indicate one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module, timing for the data reporting, a condition for the data reporting, or a periodicity for the data reporting. In some aspects, the method may be performed at a UE, where the configuration is received from a network node and the data is reported to the network node. In some aspects, the method may be performed at a network node, and the configuration may be received from a UE and the data is reported to the UE. In some aspects, the method may be performed at a first network node, and the configuration may be received from a second network node and the data is reported to the second network node. In some aspects, the method may be performed at a first UE, and the configuration may be received from a second UE and the data is reported to the second UE.
As illustrated at 1404, the device may receive an activation of data reporting for the model ID, the machine learning function, or the machine learning use case, wherein the data is reported in response to the activation. The reception may be performed, e.g., by the machine learning component 198. The activation may be in a MAC-CE or a DCI, for example. An example activation is described in connection with
As illustrated at 1412, the device may receive a deactivation of data reporting for the model ID, the machine learning function, or the machine learning use case. The deactivation may be in a MAC-CE or a DCI, for example. The reception may be performed, e.g., by the machine learning component 198. An example deactivation is described in connection with
The configuration associated with the model ID, the machine learning function, or the machine learning use case may include a data validation configuration. As illustrated at 1408, the device may validate the data prior to the reporting based on a criteria of the data validation configuration for the model ID, the machine learning function, or the machine learning use case. The validation may be performed, e.g., by the machine learning component 198. Example aspects of validation are described in connection with
In some aspects, validating the data, at 1408, may include identifying at least one of an inference or training output based on the machine learning that does not meet the validation criteria of the data validation configuration associated with the model ID, the machine learning function, or the machine learning use case. In response, the device may indicate a data validation failure according to the configuration for the model ID, the machine learning function, or the machine learning use case, e.g., as illustrated in
At 1502, the device provides a configuration for machine learning associated with a model ID, a machine learning function or, a machine learning use case. The providing of the configuration may be performed, e.g., by the machine learning configuration component 199. The configuration associated with the model ID, the machine learning function, or the machine learning use case may indicate one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module, timing for the data reporting, a condition for the data reporting, or a periodicity for the data reporting.
At 1506, the device receives a report of data based on the configuration associated with the model ID, the machine learning function, or the machine learning use case. The reception may be performed, e.g., by the machine learning configuration component 199. In some aspects, the method may be performed at a UE, where the configuration is provided to a network node and the data is received from the network node. In some aspects, the method may be performed at a network node, and the configuration may be provided to a UE and the data may be reported by the UE. In some aspects, the method may be performed at a first network node, and the configuration may be provided to a second network node that reports the data to the second network node. In some aspects, the method may be performed at a first UE, and the configuration may be provided to a second UE that reports the data is reported to the first UE. The reporting may include any of the aspects described in connection with any of
In some aspects, the method may be performed at a UE, and wherein the configuration is provided to a network node and the data is received from the network node. In some aspects, the method may be performed at a network node, and wherein the configuration is provided to a UE and the data is received from the UE. In some aspects, the method may be performed at a first network node, and wherein the configuration is provided to a second network node and the data is received from the second network node. In some aspects, the method may be performed at a first UE, and wherein the configuration is provided to a second UE and the data is received from the second UE.
At 1502, the device provides a configuration for machine learning associated with a model ID, a machine learning function or, a machine learning use case. The providing of the configuration may be performed, e.g., by the machine learning configuration component 199. The configuration associated with the model ID, the machine learning function, or the machine learning use case may indicate one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module, timing for the data reporting, a condition for the data reporting, or a periodicity for the data reporting.
At 1506, the device receives a report of data based on the configuration associated with the model ID, the machine learning function, or the machine learning use case. The reception may be performed, e.g., by the machine learning configuration component 199. In some aspects, the method may be performed at a UE, where the configuration is provided to a network node and the data is received from the network node. In some aspects, the method may be performed at a network node, and the configuration may be provided to a UE and the data may be reported by the UE. In some aspects, the method may be performed at a first network node, and the configuration may be provided to a second network node that reports the data to the second network node. In some aspects, the method may be performed at a first UE, and the configuration may be provided to a second UE that reports the data is reported to the first UE. The reporting may include any of the aspects described in connection with any of
In some aspects, the method may be performed at a UE, and wherein the configuration is provided to a network node and the data is received from the network node. In some aspects, the method may be performed at a network node, and wherein the configuration is provided to a UE and the data is received from the UE. In some aspects, the method may be performed at a first network node, and wherein the configuration is provided to a second network node and the data is received from the second network node. In some aspects, the method may be performed at a first UE, and wherein the configuration is provided to a second UE and the data is received from the second UE.
As illustrated at 1504, the device may further provide an activation of data reporting for the model ID, the machine learning function, or the machine learning use case, wherein the data is received in response to the activation. The providing of the activation may be performed, e.g., by the machine learning configuration component 199. An example activation is described in connection with
As illustrated at 1510, the device may further provide a deactivation of data reporting for the model ID, the machine learning function, or the machine learning use case. The providing of the deactivation may be performed, e.g., by the machine learning configuration component 199. An example deactivation is described in connection with
The configuration associated with the model ID may include a data validation configuration that includes one or more of: at least one rule for data validation associated with the model ID, the machine learning function, or the machine learning use case, at least one data statistic associated with the model ID, the machine learning function, or the machine learning use case, or at least one data property associated with the model ID, the machine learning function, or the machine learning use case. As illustrated at 1508, the device may receive a data validation failure indication indicating at least one of an inference or training output based on the machine learning that does not meet validation criteria of the data validation configuration associated with the model ID, the machine learning function, or the machine learning use case. The reception may be performed, e.g., by the machine learning configuration component 199. Example aspects of data validation failure are described in connection with
As discussed herein, the machine learning component 198 may be configured to process information with machine learning associated with a model ID, a machine learning function, or a machine learning use case; and reporting data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case. The machine learning component 198 may be further configured to perform any of the aspects described in connection with the flowchart in
As discussed herein, the machine learning component 198 may be configured to process information with machine learning associated with a model ID, a machine learning function, or a machine learning use case; and reporting data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case. The machine learning component 198 may be further configured to perform any of the aspects described in connection with the flowchart in
At 1804, the model designer may provide at least one of an input feature for the machine learning model, a data processing module for obtaining the input feature for the machine learning model, or a data validation scheme for the machine learning model. The data validation scheme may include one or more of: at least one rule for data validation associated with the machine learning model, at least one data statistic associated with training data for the machine learning model, at least one data statistic associated with inference data for the machine learning model, at least one data property associated with the training data for the machine learning model, or at least one data property associated with the inference data for the machine learning model.
It is understood that the specific order or hierarchy of blocks in the processes/flowcharts disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes/flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not limited to the specific order or hierarchy presented.
As described above the registration component 1920 may be configured to register a machine learning model for collection and reporting of data based on the wireless communication; and to provide at least one of an input feature for the machine learning model, a data processing module for obtaining the input feature for the machine learning model, or a data validation scheme for the machine learning model to a network entity 1904. The registration component 1920 may be configured to perform any of the aspects in
The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not limited to the aspects described herein, but are to be accorded the full scope consistent with the language claims. Reference to an element in the singular does not mean “one and only one” unless specifically so stated, but rather “one or more.” Terms such as “if,” “when,” and “while” do not imply an immediate temporal relationship or reaction. That is, these phrases, e.g., “when,” do not imply an immediate action in response to or during the occurrence of an action, but simply imply that if a condition is met then an action will occur, but without requiring a specific or immediate time constraint for the action to occur. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” include any combination of A, B, and/or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. Sets should be interpreted as a set of elements where the elements number one or more. Accordingly, for a set of X, X would include one or more elements. If a first apparatus receives data from or transmits data to a second apparatus, the data may be received/transmitted directly between the first and second apparatuses, or indirectly between the first and second apparatuses through a set of apparatuses. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are encompassed by the claims. Moreover, nothing disclosed herein is dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”
As used herein, the phrase “based on” shall not be construed as a reference to a closed set of information, one or more conditions, one or more factors, or the like. In other words, the phrase “based on A” (where “A” may be information, a condition, a factor, or the like) shall be construed as “based at least on A” unless specifically recited differently.
The following aspects are illustrative only and may be combined with other aspects or teachings described herein, without limitation.
Aspect 1 is a method of wireless communication, including: processing information with machine learning associated with a model ID, a machine learning function, or a machine learning use case; and reporting data via the wireless communication based on a configuration associated with the model ID, the machine learning function, or the machine learning use case.
In aspect 2, the method of aspect 1 further includes receiving the configuration identifying the model ID, the machine learning function, or the machine learning use case, wherein reporting the data includes transmitting the data based on a condition, timing, or periodicity indicated in the configuration for the model ID, the machine learning function, or the machine learning use case.
In aspect 3, the method of aspect 2 further includes that the method is performed at a UE, and wherein the configuration is received from a network node and the data is reported to the network node.
In aspect 4, the method of aspect 2 further includes that the method is performed at a network node, and wherein the configuration is received from a UE and the data is reported to the UE.
In aspect 5, the method of aspect 2 further includes that method is performed at a first network node, and wherein the configuration is received from a second network node and the data is reported to the second network node.
In aspect 6, the method of aspect 2 further includes that the method is performed at a first UE, and wherein the configuration is received from a second UE and the data is reported to the second UE.
In aspect 7, the method of any of aspects 1-6 further includes that the configuration associated with the model ID, the machine learning function, or the machine learning use case indicates one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module, timing for a data reporting, a condition for the data reporting, or a periodicity for the data reporting.
In aspect 8, the method of any of aspects 1-7 further includes reporting different data based on multiple configurations, each configuration associated with a different the model ID, a different machine learning function, or a different machine learning use case.
In aspect 9, the method of any of aspects 1-8 further includes receiving a data reporting activation for the model ID, the machine learning function, or the machine learning use case, wherein the data is reported in response to the activation.
In aspect 10, the method of any of aspects 1-9 further includes receiving a data reporting deactivation for the model ID, the machine learning function, or the machine learning use case; and stopping the reporting of the data for the model ID, the machine learning function, or the machine learning use case in response to the deactivation.
In aspect 11, the method of any of aspects 1-10 further includes the data is reported in at least one of an RRC message, a MAC-CE, UCI, or DCI.
In aspect 12, the method of any of aspects 1-11 further includes the configuration associated with the model ID, the machine learning function, or the machine learning use case includes a data validation configuration, the method further comprising: validating the data prior to the reporting based on a criteria of the data validation configuration for the model ID, the machine learning function, or the machine learning use case.
In aspect 13, the method of aspect 12 further includes that the data validation configuration includes one or more of: at least one rule for data validation associated with the model ID, the machine learning function, or the machine learning use case, at least one data statistic associated with the model ID, the machine learning function, or the machine learning use case, or at least one data property associated with the model ID, the machine learning function, or the machine learning use case.
In aspect 14, the method of aspect 12 or aspect 13 further includes identifying at least one of an inference or training output based on the machine learning that does not meet the criteria of the data validation configuration associated with the model ID, the machine learning function, or the machine learning use case; and indicating a data validation failure according to the configuration for the model ID, the machine learning function, or the machine learning use case.
In aspect 15, the method of aspect 14 further includes indicating the data validation failure further indicates a transition to a procedure without the machine learning.
Aspect 16 is an apparatus for wireless communication including means for performing the method of any of aspects 1-15.
Aspect 17 is an apparatus for wireless communication including a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to perform the method of any of aspects 1-15.
In aspect 18, the apparatus of aspect 16 or aspect 17 further includes at least one transceiver or at least one antenna coupled to the at least one processor.
Aspect 19 is a non-transitory computer-readable medium storing computer executable code, the code when executed by at least one processor causes the at least one processor to implement a method as in any of aspects 1-15.
Aspect 20 is a method of wireless communication, including: providing a configuration for machine learning associated with a model ID, a machine learning function or, a machine learning use case; and receiving a report of data based on the configuration associated with the model ID, the machine learning function, or the machine learning use case.
In aspect 21, the method of aspect 20 further includes that the configuration associated with the model ID, the machine learning function, or the machine learning use case indicates one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module, timing for a data reporting, a condition for the data reporting, or a periodicity for the data reporting.
In aspect 22, the method of aspect 20 or aspect 21 further includes receiving reports of different data based on multiple configurations, each configuration associated with a different model ID, a different machine learning function, or a different machine learning use case.
In aspect 23, the method of any of aspects 20-22 further includes providing a data reporting activation for the model ID, the machine learning function, or the machine learning use case, wherein the data is received in response to the activation.
In aspect 24, the method of any of aspects 20-23 further includes providing a data reporting deactivation for the model ID, the machine learning function, or the machine learning use case.
In aspect 25, the method of any of aspects 20-24 further includes that the data is received in at least one of an RRC message, a MAC-CE, UCI, or DCI.
In aspect 26, the method of any of aspects 20-25 further includes that the configuration associated with the model ID includes a data validation configuration, including one or more of: at least one rule for data validation associated with the model ID, the machine learning function, or the machine learning use case, at least one data statistic associated with the model ID, the machine learning function, or the machine learning use case, or at least one data property associated with the model ID, the machine learning function, or the machine learning use case.
In aspect 27, the method of aspect 26 further includes receiving a data validation failure indicating at least one of an inference or training output based on the machine learning that does not meet validation criteria of the data validation configuration associated with the model ID, the machine learning function, or the machine learning use case.
In aspect 28, the method of aspect 27 further includes indicating the data validation failure further indicates a transition to a procedure without the machine learning.
In aspect 29, the method of any of aspects 20-28 further includes that the method is performed at a UE, and wherein the configuration is provided to a network node and the data is received from the network node.
In aspect 30, the method of any of aspects 20-28 further includes that the method is performed at a network node, and wherein the configuration is provided to a UE and the data is received from the UE.
In aspect 31, the method of any of aspects 20-28 further includes that method is performed at a first network node, and wherein the configuration is provided to a second network node and the data is received from the second network node.
In aspect 32, the method of any of aspects 20-28 further includes that the method is performed at a first UE, and wherein the configuration is provided to a second UE and the data is received from the second UE.
Aspect 33 is an apparatus for wireless communication including means for performing the method of any of aspects 20-32.
Aspect 34 is an apparatus for wireless communication including a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to perform the method of any of aspects 20-32.
In aspect 35, the apparatus of aspect 33 or aspect 34 further includes at least one transceiver or at least one antenna coupled to the at least one processor.
Aspect 36 is a non-transitory computer-readable medium storing computer executable code, the code when executed by at least one processor causes the at least one processor to implement a method as in any of aspects 20-32.
Aspect 37 is a method of wireless communication, including: registering a machine learning model for collection and reporting of data based on the wireless communication; and providing at least one of an input feature for the machine learning model, a data processing module for obtaining the input feature for the machine learning model, or a data validation scheme for the machine learning model.
In aspect 38, the method of aspect 37 further includes that the data validation scheme includes one or more of: at least one rule for data validation associated with the machine learning model, at least one first data statistic associated with training data for the machine learning model, at least one second data statistic associated with inference data for the machine learning model, at least one first data property associated with the training data for the machine learning model, or at least one second data property associated with the inference data for the machine learning model.
Aspect 39 is an apparatus for wireless communication including means for performing the method of any of aspects 37-38.
Aspect 40 is an apparatus for wireless communication including a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to perform the method of any of aspects 37-38.
In aspect 41, the apparatus of aspect 39 or aspect 40 further includes at least one transceiver or at least one antenna coupled to the at least one processor.
Aspect 42 is a non-transitory computer-readable medium storing computer executable code, the code when executed by at least one processor causes the at least one processor to implement a method as in any of aspects 37-38.