Patent Application
20250202557
The present disclosure relates to a wireless communication system, and in more detail, relates to a method and an apparatus of transmitting and receiving channel state information in a wireless communication system.
A mobile communication system has been developed to provide a voice service while guaranteeing mobility of users. However, a mobile communication system has extended even to a data service as well as a voice service, and currently, an explosive traffic increase has caused shortage of resources and users have demanded a faster service, so a more advanced mobile communication system has been required.
The requirements of a next-generation mobile communication system at large should be able to support accommodation of explosive data traffic, a remarkable increase in a transmission rate per user, accommodation of the significantly increased number of connected devices, very low End-to-End latency and high energy efficiency. To this end, a variety of technologies such as Dual Connectivity, Massive Multiple Input Multiple Output (Massive MIMO), In-band Full Duplex, Non-Orthogonal Multiple Access (NOMA), Super wideband Support, Device Networking, etc. have been researched.
A technical object of the present disclosure is to provide a method and an apparatus of transmitting and receiving CSI.
In addition, an additional technical object of the present disclosure is to provide a method and an apparatus for determining a CSI reference resource for calculating CSI.
In addition, an additional technical object of the present disclosure is to provide a method and an apparatus for determining a channel measurement resource and/or an interference measurement resource for deriving a channel and/or interference measurement to calculate CSI.
The technical objects to be achieved by the present disclosure are not limited to the above-described technical objects, and other technical objects which are not described herein will be clearly understood by those skilled in the pertinent art from the following description.
A method performed by a user equipment (UE) in a wireless communication system according to an aspect of the present disclosure may include: receiving, from a base station, configuration information related to channel state information (CSI); calculating the CSI by deriving channel measurement and/or interference measurement based on one or more channel measurement resources (CMR) and/or one or more interference measurement resources (IMR) determined based on a CSI reference resource; and transmitting the CSI to the base station. The CSI reference resource may be determined to be a slot later than a slot in which the CSI is reported in a time domain.
A method performed by a base station in a wireless communication system according to an additional aspect of the present disclosure may include: transmitting, to a user equipment (UE), configuration information related to channel state information (CSI); and receiving the CSI from the UE. The CSI may be calculated by deriving channel measurement and/or interference measurement based on one or more channel measurement resources (CMR) and/or one or more interference measurement resources (IMR) determined based on a CSI reference resource, and the CSI reference resource may be determined to be a slot later than a slot in which the CSI is reported in a time domain.
According to an embodiment of the present disclosure, it can minimize the channel aging effect of CSI.
In addition, according to an embodiment of the present disclosure, downlink transmission performance can be improved by inferring/predicting the channel for a time close to an actual downlink transmission time.
Effects achievable by the present disclosure are not limited to the above-described effects, and other effects which are not described herein may be clearly understood by those skilled in the pertinent art from the following description.
Accompanying drawings included as part of detailed description for understanding the present disclosure provide embodiments of the present disclosure and describe technical features of the present disclosure with detailed description.
Hereinafter, embodiments according to the present disclosure will be described in detail by referring to accompanying drawings. Detailed description to be disclosed with accompanying drawings is to describe exemplary embodiments of the present disclosure and is not to represent the only embodiment that the present disclosure may be implemented. The following detailed description includes specific details to provide complete understanding of the present disclosure. However, those skilled in the pertinent art knows that the present disclosure may be implemented without such specific details.
In some cases, known structures and devices may be omitted or may be shown in a form of a block diagram based on a core function of each structure and device in order to prevent a concept of the present disclosure from being ambiguous.
In the present disclosure, when an element is referred to as being “connected”, “combined” or “linked” to another element, it may include an indirect connection relation that yet another element presents therebetween as well as a direct connection relation. In addition, in the present disclosure, a term, “include” or “have”, specifies the presence of a mentioned feature, step, operation, component and/or element, but it does not exclude the presence or addition of one or more other features, stages, operations, components, elements and/or their groups.
In the present disclosure, a term such as “first”, “second”, etc. is used only to distinguish one element from other element and is not used to limit elements, and unless otherwise specified, it does not limit an order or importance, etc. between elements.
Accordingly, within a scope of the present disclosure, a first element in an embodiment may be referred to as a second element in another embodiment and likewise, a second element in an embodiment may be referred to as a first element in another embodiment.
A term used in the present disclosure is to describe a specific embodiment, and is not to limit a claim. As used in a described and attached claim of an embodiment, a singular form is intended to include a plural form, unless the context clearly indicates otherwise. A term used in the present disclosure, “and/or”, may refer to one of related enumerated items or it means that it refers to and includes any and all possible combinations of two or more of them. In addition, “/” between words in the present disclosure has the same meaning as “and/or”, unless otherwise described.
The present disclosure describes a wireless communication network or a wireless communication system, and an operation performed in a wireless communication network may be performed in a process in which a device (e.g., a base station) controlling a corresponding wireless communication network controls a network and transmits or receives a signal, or may be performed in a process in which a terminal associated to a corresponding wireless network transmits or receives a signal with a network or between terminals.
In the present disclosure, transmitting or receiving a channel includes a meaning of transmitting or receiving information or a signal through a corresponding channel. For example, transmitting a control channel means that control information or a control signal is transmitted through a control channel. Similarly, transmitting a data channel means that data information or a data signal is transmitted through a data channel.
Hereinafter, a downlink (DL) means a communication from abase station to a terminal and an uplink (UL) means a communication from a terminal to a base station. In a downlink, a transmitter may be part of a base station and a receiver may be part of a terminal. In an uplink, a transmitter may be part of a terminal and a receiver may be part of a base station. A base station may be expressed as a first communication device and a terminal may be expressed as a second communication device. A base station (BS) may be substituted with a term such as a fixed station, a Node B, an eNB (evolved-NodeB), a gNB (Next Generation NodeB), a BTS (base transceiver system), an Access Point (AP), a Network (5G network), an AI (Artificial Intelligence) system/module, an RSU (road side unit), a robot, a drone (UAV: Unmanned Aerial Vehicle), an AR (Augmented Reality) device, a VR (Virtual Reality) device, etc. In addition, a terminal may be fixed or mobile, and may be substituted with a term such as a UE (User Equipment), an MS (Mobile Station), a UT (user terminal), an MSS (Mobile Subscriber Station), an SS (Subscriber Station), an AMS (Advanced Mobile Station), a WT (Wireless terminal), an MTC (Machine-Type Communication) device, an M2M (Machine-to-Machine) device, a D2D (Device-to-Device) device, a vehicle, an RSU (road side unit), a robot, an AI (Artificial Intelligence) module, a drone (UAV: Unmanned Aerial Vehicle), an AR (Augmented Reality) device, a VR (Virtual Reality) device, etc.
The following description may be used for a variety of radio access systems such as CDMA, FDMA, TDMA, OFDMA, SC-FDMA, etc. CDMA may be implemented by a wireless technology such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA may be implemented by a radio technology such as GSM (Global System for Mobile communications)/GPRS (General Packet Radio Service)/EDGE (Enhanced Data Rates for GSM Evolution). OFDMA may be implemented by a radio technology such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (Evolved UTRA), etc. UTRA is a part of a UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is a part of an E-UMTS (Evolved UMTS) using E-UTRA and LTE-A (Advanced)/LTE-A pro is an advanced version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an advanced version of 3GPP LTE/LTE-A/LTE-A pro.
To clarify description, it is described based on a 3GPP communication system (e.g., LTE-A, NR), but a technical idea of the present disclosure is not limited thereto. LTE means a technology after 3GPP TS (Technical Specification) 36.xxx Release 8. In detail, an LTE technology in or after 3GPP TS 36.xxx Release 10 is referred to as LTE-A and an LTE technology in or after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR means a technology in or after TS 38.xxx Release 15. LTE/NR may be referred to as a 3GPP system. “xxx” means a detailed number for a standard document. LTE/NR may be commonly referred to as a 3GPP system. For a background art, a term, an abbreviation, etc. used to describe the present disclosure, matters described in a standard document disclosed before the present disclosure may be referred to. For example, the following document may be referred to.
For 3GPP LTE, TS 36.211 (physical channels and modulation), TS 36.212 (multiplexing and channel coding), TS 36.213 (physical layer procedures), TS 36.300 (overall description), TS 36.331 (radio resource control) may be referred to.
For 3GPP NR, TS 38.211 (physical channels and modulation), TS 38.212 (multiplexing and channel coding), TS 38.213 (physical layer procedures for control), TS 38.214 (physical layer procedures for data), TS 38.300 (NR and NG-RAN (New Generation-Radio Access Network) overall description), TS 38.331 (radio resource control protocol specification) may be referred to.
Abbreviations of terms which may be used in the present disclosure is defined as follows.
As more communication devices have required a higher capacity, a need for an improved mobile broadband communication compared to the existing radio access technology (RAT) has emerged. In addition, massive MTC (Machine Type Communications) providing a variety of services anytime and anywhere by connecting a plurality of devices and things is also one of main issues which will be considered in a next-generation communication.
Furthermore, a communication system design considering a service/a terminal sensitive to reliability and latency is also discussed. As such, introduction of a next-generation RAT considering eMBB (enhanced mobile broadband communication), mMTC (massive MTC), URLLC (Ultra-Reliable and Low Latency Communication), etc. is discussed and, for convenience, a corresponding technology is referred to as NR in the present disclosure. NR is an expression which represents an example of a 5G RAT.
A new RAT system including NR uses an OFDM transmission method or a transmission method similar to it. A new RAT system may follow OFDM parameters different from OFDM parameters of LTE. Alternatively, a new RAT system follows a numerology of the existing LTE/LTE-A as it is, but may support a wider system bandwidth (e.g., 100 MHz). Alternatively, one cell may support a plurality of numerologies. In other words, terminals which operate in accordance with different numerologies may coexist in one cell.
A numerology corresponds to one subcarrier spacing in a frequency domain. As a reference subcarrier spacing is scaled by an integer N, a different numerology may be defined.
In reference to
A NR system may support a plurality of numerologies. Here, a numerology may be defined by a subcarrier spacing and a cyclic prefix (CP) overhead. Here, a plurality of subcarrier spacings may be derived by scaling a basic (reference) subcarrier spacing by an integer N (or, μ). In addition, although it is assumed that a very low subcarrier spacing is not used in a very high carrier frequency, a used numerology may be selected independently from a frequency band. In addition, a variety of frame structures according to a plurality of numerologies may be supported in a NR system.
Hereinafter, an OFDM numerology and frame structure which may be considered in a NR system will be described. A plurality of OFDM numerologies supported in a NR system may be defined as in the following Table 1.
NR supports a plurality of numerologies (or subcarrier spacings (SCS)) for supporting a variety of 5G services. For example, when a SCS is 15 kHz, a wide area in traditional cellular bands is supported, and when a SCS is 30 kHz/60 kHz, dense-urban, lower latency and a wider carrier bandwidth are supported, and when a SCS is 60 kHz or higher, a bandwidth wider than 24.25 GHz is supported to overcome a phase noise.
An NR frequency band is defined as a frequency range in two types (FR1, FR2). FR1, FR2 may be configured as in the following Table 2. In addition, FR2 may mean a millimeter wave (mmW).
Regarding a frame structure in an NR system, a size of a variety of fields in a time domain is expresses as a multiple of a time unit of Tc=1/(Δfmax·Nf). Here, Δfmax is 480·103 Hz and Nf is 4096. Downlink and uplink transmission is configured (organized) with a radio frame having a duration of Tf=1/(ΔfmaxNf/100)·Tc=10 ms. Here, a radio frame is configured with 10 subframes having a duration of Tsf=(ΔfmaxNf/1000)·Tc=1 ms, respectively. In this case, there may be one set of frames for an uplink and one set of frames for a downlink. In addition, transmission in an uplink frame No. i from a terminal should start earlier by TTA=(NTA+NTA,offset)Tc than a corresponding downlink frame in a corresponding terminal starts. For a subcarrier spacing configuration p, slots are numbered in an increasing order of nsμ∈{0, . . . , Nslotsubframe,μ−1} in a subframe and are numbered in an increasing order of ns,fμ∈{0, . . . , Nslotframe,μ−1} in a radio frame. One slot is configured with Nsymbslot consecutive OFDM symbols and Nsymbslot is determined according to CP. A start of a slot nsμ in a subframe is temporally arranged with a start of an OFDM symbol nsμNsymbslot in the same subframe. All terminals may not perform transmission and reception at the same time, which means that all OFDM symbols of a downlink slot or an uplink slot may not be used.
Table 3 represents the number of OFDM symbols per slot (Nsymbslot), the number of slots per radio frame (Nslotframe,μ) and the number of slots per subframe (Nslotsubframe,μ) in a normal CP and Table 4 represents the number of OFDM symbols per slot, the number of slots per radio frame and the number of slots per subframe in an extended CP.
Regarding a physical resource in a NR system, an antenna port, a resource grid, a resource element, a resource block, a carrier part, etc. may be considered. Hereinafter, the physical resources which may be considered in an NR system will be described in detail.
First, in relation to an antenna port, an antenna port is defined so that a channel where a symbol in an antenna port is carried can be inferred from a channel where other symbol in the same antenna port is carried. When a large-scale property of a channel where a symbol in one antenna port is carried may be inferred from a channel where a symbol in other antenna port is carried, it may be said that 2 antenna ports are in a QC/QCL (quasi co-located or quasi co-location) relationship. In this case, the large-scale property includes at least one of delay spread, doppler spread, frequency shift, average received power, received timing.
In reference to
Point A plays a role as a common reference point of a resource block grid and is obtained as follows.
Common resource blocks are numbered from 0 to the top in a frequency domain for a subcarrier spacing configuration μ. The center of subcarrier 0 of common resource block 0 for a subcarrier spacing configuration μ is identical to ‘point A’. A relationship between a common resource block number nCRBμ and a resource element (k,l) for a subcarrier spacing configuration μ in a frequency domain is given as in the following Equation 1.
In Equation 1, k is defined relatively to point A so that k=0 corresponds to a subcarrier centering in point A. Physical resource blocks are numbered from 0 to NBWP,isize,μ−1 in a bandwidth part (BWP) and i is a number of a BWP. A relationship between a physical resource block nPRB and a common resource block nCRB in BWP i is given by the following Equation 2.
NBWP,istart,μ is a common resource block that a BWP starts relatively to common resource block 0.
In reference to
A carrier includes a plurality of subcarriers in a frequency domain. An RB (Resource Block) is defined as a plurality of (e.g., 12) consecutive subcarriers in a frequency domain. A BWP (Bandwidth Part) is defined as a plurality of consecutive (physical) resource blocks in a frequency domain and may correspond to one numerology (e.g., an SCS, a CP length, etc.). A carrier may include a maximum N (e.g., 5) BWPs. A data communication may be performed through an activated BWP and only one BWP may be activated for one terminal. In a resource grid, each element is referred to as a resource element (RE) and one complex symbol may be mapped.
In an NR system, up to 400 MHz may be supported per component carrier (CC). If a terminal operating in such a wideband CC always operates turning on a radio frequency (FR) chip for the whole CC, terminal battery consumption may increase. Alternatively, when several application cases operating in one wideband CC (e.g., eMBB, URLLC, Mmtc, V2X, etc.) are considered, a different numerology (e.g., a subcarrier spacing, etc.) may be supported per frequency band in a corresponding CC. Alternatively, each terminal may have a different capability for the maximum bandwidth. By considering it, a base station may indicate a terminal to operate only in a partial bandwidth, not in a full bandwidth of a wideband CC, and a corresponding partial bandwidth is defined as a bandwidth part (BWP) for convenience. A BWP may be configured with consecutive RBs on a frequency axis and may correspond to one numerology (e.g., a subcarrier spacing, a CP length, a slot/a mini-slot duration).
Meanwhile, a base station may configure a plurality of BWPs even in one CC configured to a terminal. For example, a BWP occupying a relatively small frequency domain may be configured in a PDCCH monitoring slot, and a PDSCH indicated by a PDCCH may be scheduled in a greater BWP. Alternatively, when UEs are congested in a specific BWP, some terminals may be configured with other BWP for load balancing. Alternatively, considering frequency domain inter-cell interference cancellation between neighboring cells, etc., some middle spectrums of a full bandwidth may be excluded and BWPs on both edges may be configured in the same slot. In other words, a base station may configure at least one DL/UL BWP to a terminal associated with a wideband CC. A base station may activate at least one DL/UL BWP of configured DL/UL BWP(s) at a specific time (by L1 signaling or MAC CE (Control Element) or RRC signaling, etc.). In addition, abase station may indicate switching to other configured DL/UL BWP (by L1 signaling or MAC CE or RRC signaling, etc.). Alternatively, based on a timer, when a timer value is expired, it may be switched to a determined DL/UL BWP. Here, an activated DL/UL BWP is defined as an active DL/UL BWP. But, a configuration on a DL/UL BWP may not be received when a terminal performs an initial access procedure or before a RRC connection is set up, so a DL/UL BWP which is assumed by a terminal under these situations is defined as an initial active DL/UL BWP.
In a wireless communication system, a terminal receives information through a downlink from a base station and transmits information through an uplink to a base station. Information transmitted and received by a base station and a terminal includes data and a variety of control information and a variety of physical channels exist according to a type/a usage of information transmitted and received by them.
When a terminal is turned on or newly enters a cell, it performs an initial cell search including synchronization with a base station or the like (S601). For the initial cell search, a terminal may synchronize with a base station by receiving a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from a base station and obtain information such as a cell identifier (ID), etc. After that, a terminal may obtain broadcasting information in a cell by receiving a physical broadcast channel (PBCH) from a base station. Meanwhile, a terminal may check out a downlink channel state by receiving a downlink reference signal (DL RS) at an initial cell search stage.
A terminal which completed an initial cell search may obtain more detailed system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) according to information carried in the PDCCH (S602).
Meanwhile, when a terminal accesses to a base station for the first time or does not have a radio resource for signal transmission, it may perform a random access (RACH) procedure to a base station (S603 to S606). For the random access procedure, a terminal may transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S603 and S605) and may receive a response message for a preamble through a PDCCH and a corresponding PDSCH (S604 and S606). A contention based RACH may additionally perform a contention resolution procedure.
A terminal which performed the above-described procedure subsequently may perform PDCCH/PDSCH reception (S607) and PUSCH (Physical Uplink Shared Channel)/PUCCH (physical uplink control channel) transmission (S608) as a general uplink/downlink signal transmission procedure. In particular, a terminal receives downlink control information (DCI) through a PDCCH. Here, DCI includes control information such as resource allocation information for a terminal and a format varies depending on its purpose of use.
Meanwhile, control information which is transmitted by a terminal to a base station through an uplink or is received by a terminal from a base station includes a downlink/uplink ACK/NACK (Acknowledgement/Non-Acknowledgement) signal, a CQI (Channel Quality Indicator), a PMI (Precoding Matrix Indicator), a RI (Rank Indicator), etc. For a 3GPP LTE system, a terminal may transmit control information of the above-described CQI/PMI/RI, etc. through a PUSCH and/or a PUCCH.
Table 5 represents an example of a DCI format in an NR system.
In reference to Table 5, DCI formats 0_0, 0_1 and 0_2 may include resource information (e.g., UL/SUL (Supplementary UL), frequency resource allocation, time resource allocation, frequency hopping, etc.), information related to a transport block (TB) (e.g., MCS (Modulation Coding and Scheme), a NDI (New Data Indicator), a RV (Redundancy Version), etc.), information related to a HARQ (Hybrid-Automatic Repeat and request) (e.g., a process number, a DAI (Downlink Assignment Index), PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., DMRS sequence initialization information, an antenna port, a CSI request, etc.), power control information (e.g., PUSCH power control, etc.) related to scheduling of a PUSCH and control information included in each DCI format may be pre-defined.
DCI format 0_0 is used for scheduling of a PUSCH in one cell. Information included in DCI format 0_0 is CRC (cyclic redundancy check) scrambled by a C-RNTI (Cell Radio Network Temporary Identifier) or a CS-RNTI (Configured Scheduling RNTI) or a MCS-C-RNTI (Modulation Coding Scheme Cell RNTI) and transmitted.
DCI format 0_1 is used to indicate scheduling of one or more PUSCHs or configure grant (CG) downlink feedback information to a terminal in one cell. Information included in DCI format 0_1 is CRC scrambled by a C-RNTI or a CS-RNTI or a SP-CSI-RNTI (Semi-Persistent CSI RNTI) or a MCS-C-RNTI and transmitted.
DCI format 0_2 is used for scheduling of a PUSCH in one cell. Information included in DCI format 0_2 is CRC scrambled by a C-RNTI or a CS-RNTI or a SP-CSI-RNTI or a MCS-C-RNTI and transmitted.
Next, DCI formats 1_0, 1_1 and 1_2 may include resource information (e.g., frequency resource allocation, time resource allocation, VRB (virtual resource block)-PRB (physical resource block) mapping, etc.), information related to a transport block (TB) (e.g., MCS, NDI, RV, etc.), information related to a HARQ (e.g., a process number, DAI, PDSCH-HARQ feedback timing, etc.), information related to multiple antennas (e.g., an antenna port, a TCI (transmission configuration indicator), a SRS (sounding reference signal) request, etc.), information related to a PUCCH (e.g., PUCCH power control, a PUCCH resource indicator, etc.) related to scheduling of a PDSCH and control information included in each DCI format may be pre-defined.
DCI format 1_0 is used for scheduling of a PDSCH in one DL cell. Information included in DCI format 1_0 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.
DCI format 1_1 is used for scheduling of a PDSCH in one cell. Information included in DCI format 1_1 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.
DCI format 1_2 is used for scheduling of a PDSCH in one cell. Information included in DCI format 1_2 is CRC scrambled by a C-RNTI or a CS-RNTI or a MCS-C-RNTI and transmitted.
With the technological advancement of artificial intelligence/machine learning (AI/ML), node(s) and UE(s) in a wireless communication network are becoming more intelligent/advanced. In particular, due to the intelligence of networks/base stations, it is expected that it will be possible to rapidly optimize and derive/apply various network/base station decision parameter values (e.g., transmission/reception power of each base station, transmission power of each UE, precoder/beam of base station/UE, time/frequency resource allocation for each UE, duplex method of each base station, etc.) according to various environmental parameters (e.g., distribution/location of base stations, distribution/location/material of buildings/furniture, etc., location/movement direction/speed of UEs, climate information, etc.). Following this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering introduction, and studies on this are also actively underway.
The AI-related descriptions and operations described below can be applied in combination with the methods proposed in the present disclosure described later, or can be supplemented to clarify the technical features of the methods proposed in the present disclosure.
Referring to
Machine Learning (ML) refers to a technology in which machines learn patterns for decision-making from data on their own without explicitly programming rules.
Deep Learning is an artificial neural network-based model that allows a machine to perform feature extraction and decision from unstructured data at once. The algorithm relies on a multi-layer network of interconnected nodes for feature extraction and transformation, inspired by the biological nervous system, or Neural Network. Common deep learning network architectures include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).
AI (or referred to as AI/ML) can be narrowly referred to as artificial intelligence based on deep learning, but is not limited to this in the present disclosure. That is, in the present disclosure, AI (or AI/ML) may collectively refer to automation technologies applied to intelligent machines (e.g., UE, RAN, network nodes, etc.) that can perform tasks like humans.
AI (or AI/ML) can be classified according to various criteria as follows.
Offline learning follows a sequential procedure of database collection, learning, and prediction. In other words, collection and learning can be performed offline, and the completed program can be installed in the field and used for prediction work. For offline learning, the system does not learn incrementally, the learning is performed using all available collected data and applied to the system without further learning. If learning about new data is necessary, learning can begin again using all new data.
This refers to a method of gradually improving performance by incrementally additional learning with data generated in real time, recently, by taking advantage of the fact that data that can be used for learning continues to be generated through the Internet, Learning is performed in real time for each (bundle) of specific data collected online, allowing the system to quickly adapt to changing data.
Only online learning is used to build an AI system, so learning may be performed only with data generated in real time, or after offline learning is performed using a predetermined data set, additional learning may be performed using additional real-time data (online+offline learning).
In centralized learning, training data collected from a plurality of different nodes is reported to a centralized node, all data resources/storage/learning (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.) are performed in one centralized node.
Federated learning is a collective model built on data that exists across distributed data owners. Instead of collecting data into a model, AI/ML models are imported into a data source, allowing local nodes/individual devices to collect data and train their own copies of the model, eliminating the need to report the source data to a central node. In federated learning, the parameters/weights of an AI/ML model can be sent back to the centralized node to support general model training. Federated learning has advantages in terms of increased computation speed and information security. In other words, the process of uploading personal data to the central server is unnecessary, preventing leakage and misuse of personal information.
Distributed learning refers to the concept in which machine learning processes are scaled and distributed across a cluster of nodes. Training models are split and shared across multiple nodes operating simultaneously to speed up model training.
Supervised learning is a machine learning task that aims to learn a mapping function from input to output, given a labeled data set. The input data is called training data and has known labels or results. An example of supervised learning is as follows.
Supervised learning can be further grouped into regression and classification problems, where classification is predicting a label and regression is predicting a quantity.
Unsupervised learning is a machine learning task that aims to learn features that describe hidden structures in unlabeled data. The input data is not labeled and there are no known results. Some examples of unsupervised learning include K-means clustering, Principal Component Analysis (PCA), nonlinear Independent Component Analysis (ICA), and Long-Short-Term Memory (LSTM).
In reinforcement learning (RL), the agent aims to optimize long-term goals by interacting with the environment based on a trial and error process, and is goal-oriented learning based on interaction with the environment. An example of the RL algorithm is as follows.
Additionally, reinforcement learning can be grouped into model-based reinforcement learning and model-free reinforcement learning as follows.
Additionally, RL algorithm can also be classified into value-based RL vs. policy-based RL, policy-based RL vs. non-policy RL, etc.
Hereinafter, representative models of deep learning will be exemplified.
A feed-forward neural network (FFNN) is composed of an input layer, a hidden layer, and an output layer.
In FFNN, information is transmitted only from the input layer to the output layer, and if there is a hidden layer, it passes through it.
A recurrent neural network (RNN) is a type of artificial neural network in which hidden nodes are connected to directed edges to form a directed cycle. This model is suitable for processing data that appears sequentially, such as voice and text.
In
One type of RNN is LSTM (Long Short-Term Memory), which has a structure that adds a cell-state to the hidden state of the RNN. LSTM can erase unnecessary memories by adding an input gate, forgetting gate, and output gate to the RNN cell (memory cell of the hidden layer). LSTM adds cell state compared to RNN.
Convolutional neural network (CNN) is used for two purposes: reducing model complexity and extracting good features by applying convolution operations commonly used in the image processing or image processing fields.
Auto encoder refers to a neural network that receives a feature vector x (x1, x2, x3, . . . ) as input and outputs the same or similar vector x′ (x′1, x′2, x′3, . . . )′.
Auto encoder has the same characteristics as the input node and output node. Since the auto encoder reconstructs the input, the output can be referred to as reconstruction. Additionally, auto encoder is a type of unsupervised learning.
The loss function of the auto encoder illustrated in
Hereinafter, for a more specific explanation of AI (or AI/ML), terms can be defined as follows.
AI/ML Training: An online or offline process to train an AI model by learning features and patterns that best present data and get the trained AI/ML model for inference.
AI/ML Inference: A process of using a trained AI/ML model to make a prediction or guide the decision based on collected data and AI/ML model.
Referring to
Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI model.
The Data Collection function (10) performs data preparation based on input data and provides input data processed through data preparation. Here, the Data Collection function (10) does not perform specific data preparation (e.g., data pre-processing and cleaning, formatting and transformation) for each AI algorithm, and data preparation common to AI algorithms can be performed.
After performing the data preparation process, the Model Training function (10) provides Training Data (11) to the Model Training function (20) and provides Inference Data (12) to the Model Inference function (30). Here, Training Data (11) is data required as input for the AI Model Training function (20). Inference Data (12) is data required as input for the AI Model Inference function (30).
The Data Collection function (10) may be performed by a single entity (e.g., UE, RAN node, network node, etc.), but may also be performed by a plurality of entities. In this case, Training Data (11) and Inference Data (12) can be provided from a plurality of entities to the Model Training function (20) and the Model Inference function (30), respectively.
Model Training function (20) is a function that performs the AI model training, validation, and testing which may generate model performance metrics as part of the model testing procedure. The Model Training function (20) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data (11) delivered by a Data Collection function (10), if required.
Here, Model Deployment/Update (13) is used to initially deploy a trained, validated, and tested AI model to the Model Inference function (30) or to deliver an updated model to the Model Inference function (30).
Model Inference function (30) is a function that provides AI model inference output (16) (e.g., predictions or decisions). Model Inference function (30) may provide Model Performance Feedback (14) to Model Training function (20) when applicable. The Model Inference function (30) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data (12) delivered by a Data Collection function (10), if required.
Here, Output (16) refers to the inference output of the AI model produced by a Model Inference function (30), and details of inference output may be use case specific.
Model Performance Feedback (14) may be used for monitoring the performance of the AI model, when available, and this feedback may be omitted.
Actor function (40) is a function that receives the Output (16) from the Model Inference function (30) and triggers or performs corresponding actions. The Actor function (40) may trigger actions directed to other entities (e.g., one or more UEs, one or more RAN nodes, one or more network nodes, etc) or to itself.
Feedback (15) may be used to derive Training data (11), Inference data (12) or to monitor the performance of the AI Model and its impact to the network, etc.
Meanwhile, the definitions of training/validation/test in the data set used in AI/ML can be divided as follows.
Training data: refers to a data set for learning a model.
Validation data: This refers to a data set for verifying a model for which learning has already been completed. In other words, it usually refers to a data set used to prevent over-fitting of the training data set.
It also refers to a data set for selecting the best among various models learned during the learning process. Therefore, it can also be considered as a type of learning.
In the case of the data set, if the training set is generally divided, within the entire training set, training data and validation data can be divided into 8:2 or 7:3, and if testing is included, 6:2:2 (training: validation: test) can be used.
Depending on the capability of the AI/ML function between a base station and a UE, a cooperation level can be defined as follows, and modifications can be made by combining the following multiple levels or separating any one level.
Cat 0a) No collaboration framework: AI/ML algorithm is purely implementation-based and do not require any air interface changes.
Cat 0b) This level corresponds to a framework without cooperation but with a modified air interface tailored to efficient implementation-based AI/ML algorithm.
Cat 1) This involves inter-node support to improve the AI/ML algorithm of each node. This applies if a UE receives support from a gNB (for training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes is not required.
Cat 2) Joint ML tasks between a UE and a gNB may be performed. This level requires AI/ML model command and an exchange between network nodes.
The functions previously illustrated in
Alternatively, the function illustrated in
Alternatively, any one of the functions illustrated in
In addition to the Model Inference function, the Model Training function, the Actor, and the Data Collection function are respectively split into multiple parts depending on the current task and environment, and can be performed by multiple entities collaborating.
For example, computation-intensive and energy-intensive parts may be performed at a network endpoint, while parts sensitive to personal information and delay-sensitive parts may be performed at an end device. In this case, an end device can execute a task/model from input data to a specific part/layer and then transmit intermediate data to a network endpoint. A network endpoint executes the remaining parts/layers and provides inference outputs to one or more devices that perform an action/task.
Step 1: RAN Node 1 and RAN Node 2 transmit input data (i.e., Training data) for AI Model Training to the network node. Here, RAN Node 1 and RAN Node 2 may transmit the data collected from the UE (e.g., UE measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, UE location, speed, etc.) to the network node.
Step 2: The network node trains the AI Model using the received training data.
Step 3: The network node distributes/updates the AI Model to RAN Node 1 and/or RAN Node 2. RAN Node 1 (and/or RAN Node 2) may continue to perform model training based on the received AI Model.
For convenience of explanation, it is assumed that the AI Model has been distributed/updated only to RAN Node 1.
Step 4: RAN Node 1 receives input data (i.e., Inference data) for AI Model Inference from the UE and RAN Node 2.
Step 5: RAN Node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).
Step 6: If applicable, RAN Node 1 may send model performance feedback to the network node.
Step 7: RAN Node 1, RAN Node 2, and UE (or ‘RAN Node 1 and UE’, or ‘RAN Node 1 and RAN Node 2’) perform an action based on the output data. For example, in the case of load balancing operation, the UE may move from RAN node 1 to RAN node 2.
Step 8: RAN Node 1 and RAN Node 2 transmit feedback information to the network node.
Step 1: The UE and RAN Node 2 transmit input data (i.e., Training data) for AI Model Training to RAN Node 1.
Step 2: RAN Node 1 trains the AI Model using the received training data.
Step 3: RAN Node 1 receives input data (i.e., Inference data) for AI Model Inference from the UE and RAN Node 2.
Step 4: RAN Node 1 performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).
Step 5: RAN Node 1, RAN Node 2, and the UE (or ‘RAN Node 1 and UE’, or ‘RAN Node 1 and RAN Node 2’) perform an action based on the output data. For example, in the case of load balancing operation, the UE may move from RAN node 1 to RAN node 2.
Step 6: RAN node 2 transmits feedback information to RAN node 1.
Step 1: The UE transmits input data (i.e., Training data) for AI Model Training to the RAN node. Here, the RAN node may collect data (e.g., measurements of the UE related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, location of the UE, speed, etc.) from various UEs and/or from other RAN nodes.
Step 2: The RAN node trains the AI Model using the received training data.
Step 3: The RAN node distributes/updates the AI Model to the UE. The UE may continue to perform model training based on the received AI Model.
Step 4: The UE receives input data (i.e., Inference data) for AI Model Inference from the RAN node (and/or from other UEs).
Step 5: The UE performs AI Model Inference using the received Inference data to generate output data (e.g., prediction or decision).
Step 6: If applicable, the UE may transmit model performance feedback to the RAN node.
Step 7: The UE and the RAN node perform an action based on output data.
Step 8: The UE transmits feedback information to the RAN node.
In an NR (New Radio) system, a CSI-RS (channel state information-reference signal) is used for time and/or frequency tracking, CSI computation, L1 (layer 1)-RSRP (reference signal received power) computation and mobility. Here, CSI computation is related to CSI acquisition and L1-RSRP computation is related to beam management (BM).
CSI (channel state information) collectively refers to information which may represent quality of a radio channel (or also referred to as a link) formed between a terminal and an antenna port.
To perform one of the usages of a CSI-RS, a terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) through RRC (radio resource control) signaling.
The configuration information related to CSI may include at least one of information related to a CSI-IM (interference management) resource, information related to CSI measurement configuration, information related to CSI resource configuration, information related to a CSI-RS resource or information related to CSI report configuration.
i) Information related to a CSI-IM resource may include CSI-IM resource information, CSI-IM resource set information, etc. A CSI-IM resource set is identified by a CSI-IM resource set ID (identifier) and one resource set includes at least one CSI-IM resource. Each CSI-IM resource is identified by a CSI-IM resource ID.
ii) Information related to CSI resource configuration may be expressed as CSI-ResourceConfig IE. Information related to a CSI resource configuration defines a group which includes at least one of an NZP (non zero power) CSI-RS resource set, a CSI-IM resource set or a CSI-SSB resource set. In other words, the information related to a CSI resource configuration may include a CSI-RS resource set list and the CSI-RS resource set list may include at least one of a NZP CSI-RS resource set list, a CSI-IM resource set list or a CSI-SSB resource set list. A CSI-RS resource set is identified by a CSI-RS resource set ID and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.
Parameters representing a usage of a CSI-RS (e.g., a ‘repetition’ parameter related to BM, a ‘trs-Info’ parameter related to tracking) may be configured per NZP CSI-RS resource set.
iii) Information related to a CSI report configuration includes a report configuration type (reportConfigType) parameter representing a time domain behavior and a report quantity (reportQuantity) parameter representing CSI-related quantity for a report. The time domain behavior may be periodic, aperiodic or semi-persistent.
The CSI measurement may include (1) a process in which a terminal receives a CSI-RS and (2) a process in which CSI is computed through a received CSI-RS and detailed description thereon is described after.
For a CSI-RS, RE (resource element) mapping of a CSI-RS resource in a time and frequency domain is configured by higher layer parameter CSI-RS-ResourceMapping.
A terminal reports the measured CSI to a base station.
In this case, when quantity of CSI-ReportConfig is configured as ‘none (or No report)’, the terminal may omit the report. But, although the quantity is configured as ‘none (or No report)’, the terminal may perform a report to a base station. When the quantity is configured as ‘none’, an aperiodic TRS is triggered or repetition is configured. In this case, only when repetition is configured as ‘ON’, a report of the terminal may be omitted.
An NR system supports more flexible and dynamic CSI measurement and reporting. Here, the CSI measurement may include a procedure of receiving a CSI-RS and acquiring CSI by computing a received CSI-RS.
As a time domain behavior of CSI measurement and reporting, aperiodic/semi-persistent/periodic CM (channel measurement) and IM (interference measurement) are supported. 4-port NZP CSI-RS RE pattern is used for CSI-IM configuration.
CSI-IM based IMR of NR has a design similar to CSI-IM of LTE and is configured independently from ZP CSI-RS resources for PDSCH rate matching. In addition, each port emulates an interference layer having (a desirable channel and) a precoded NZP CSI-RS in NZP CSI-RS-based IMR. As it is about intra-cell interference measurement for a multi-user case, MU interference is mainly targeted.
A base station transmits a precoded NZP CSI-RS to a terminal in each port of configured NZP CSI-RS based IMR.
A terminal assumes a channel/interference layer and measures interference for each port in a resource set.
When there is no PMI and RI feedback for a channel, a plurality of resources are configured in a set and a base station or a network indicates a subset of NZP CSI-RS resources through DCI for channel/interference measurement.
A resource setting and a resource setting configuration are described in more detail.
Each CSI resource setting ‘CSI-ResourceConfig’ includes a configuration for a S≥1 CSI resource set (given by a higher layer parameter csi-RS-ResourceSetList). A CSI resource setting corresponds to CSI-RS-resourcesetlist. Here, S represents the number of configured CSI-RS resource sets. Here, a configuration for a S≥1 CSI resource set includes each CSI resource set including CSI-RS resources (configured with a NZP CSI-RS or CSI-IM) and a SS/PBCH block (SSB) resource used for L1-RSRP computation.
Each CSI resource setting is positioned at a DL BWP (bandwidth part) identified by a higher layer parameter bwp-id. In addition, all CSI resource settings linked to a CSI reporting setting have the same DL BWP.
A time domain behavior of a CSI-RS resource in a CSI resource setting included in a CSI-ResourceConfig IE may be indicated by a higher layer parameter resourceType and may be configured to be aperiodic, periodic or semi-persistent. For a periodic and semi-persistent CSI resource setting, the number (S) of configured CSI-RS resource sets is limited to ‘1’. For a periodic and semi-persistent CSI resource setting, configured periodicity and a slot offset are given by a numerology of an associated DL BWP as given by bwp-id.
When UE is configured with a plurality of CSI-ResourceConfigs including the same NZP CSI-RS resource ID, the same time domain behavior is configured for CSI-ResourceConfig.
When UE is configured with a plurality of CSI-ResourceConfigs including the same CSI-IM resource ID, the same time domain behavior is configured for CSI-ResourceConfig.
One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured through higher layer signaling as follows.
In other words, a CMR (channel measurement resource) may be a NZP CSI-RS for CSI acquisition and an IMR (Interference measurement resource) may be a NZP CSI-RS for CSI-IM and IM.
In this case, CSI-IM (or a ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.
In addition, an NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.
UE may assume that CSI-RS resource(s) for channel measurement and CSI-IM/NZP CSI-RS resource(s) for interference measurement configured for one CSI reporting are ‘QCL-TypeD’ per resource.
As described, a resource setting may mean a resource set list.
For aperiodic CSI, each trigger state configured by using a higher layer parameter CSI-AperiodicTriggerState is associated with one or a plurality of CSI-ReportConfigs that each CSI-ReportConfig is linked to a periodic, semi-persistent or aperiodic resource setting.
One reporting setting may be connected to up to 3 resource settings.
For semi-persistent or periodic CSI, each CSI-ReportConfig is linked to a periodic or semi-persistent resource setting.
When interference measurement is performed in CSI-IM, each CSI-RS resource for channel measurement is associated with a CSI-IM resource per resource in an order of CSI-RS resources and CSI-IM resources in a corresponding resource set. The number of CSI-RS resources for channel measurement is the same as the number of CSI-IM resources.
In addition, when interference measurement is performed in an NZP CSI-RS, UE does not expect to be configured with one or more NZP CSI-RS resources in an associated resource set in a resource setting for channel measurement.
A terminal configured with a higher layer parameter nzp-CSI-RS-ResourcesForInterference does not expect that 18 or more NZP CSI-RS ports will be configured in a NZP CSI-RS resource set.
For CSI measurement, a terminal assumes the followings.
For a CSI report, a time and frequency resource which may be used by UE are controlled by a base station.
CSI (channel state information) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), a SS/PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI) or L1-RSRP.
For CQI, PMI, CRI, SSBRI, LI, RI, L1-RSRP, a terminal is configured by a higher layer with N≥1 CSI-ReportConfig reporting setting, M≥1 CSI-ResourceConfig resource setting and a list of one or two trigger states (provided by aperiodicTriggerStateList and semiPersistentOnPUSCH-TriggerStateList). Each trigger state in the aperiodicTriggerStateList includes a associated CSI-ReportConfigs list which indicates a channel and optional resource set IDs for interference. In semiPersistentOnPUSCH-TriggerStateList, one associated CSI-ReportConfig is included in each trigger state.
In addition, a time domain behavior of CSI reporting supports periodic, semi-persistent, aperiodic.
i) Periodic CSI reporting is performed in a short PUCCH, a long PUCCH. Periodicity and a slot offset of periodic CSI reporting may be configured by RRC and refers to a CSI-ReportConfig IE.
ii) SP (semi-periodic) CSI reporting is performed in a short PUCCH, a long PUCCH, or a PUSCH.
For SP CSI in a short/long PUCCH, periodicity and a slot offset are configured by RRC and a CSI report is activated/deactivated by separate MAC CE/DCI.
For SP CSI in a PUSCH, periodicity of SP CSI reporting is configured by RRC, but a slot offset is not configured by RRC and SP CSI reporting is activated/deactivated by DCI (format 0_1). For SP CSI reporting in a PUSCH, a separated RNTI (SP-CSI C-RNTI) is used.
An initial CSI report timing follows a PUSCH time domain allocation value indicated by DCI and a subsequent CSI report timing follows a periodicity configured by RRC.
DCI format 0_1 may include a CSI request field and activate/deactivate a specific configured SP-CSI trigger state. SP CSI reporting has activation/deactivation equal or similar to a mechanism having data transmission in a SPS PUSCH.
iii) Aperiodic CSI reporting is performed in a PUSCH and is triggered by DCI. In this case, information related to trigger of aperiodic CSI reporting may be delivered/indicated/configured through MAC-CE.
For AP CSI having an AP CSI-RS, AP CSI-RS timing is configured by RRC and timing for AP CSI reporting is dynamically controlled by DCI.
In NR, a method of dividing and reporting CSI in a plurality of reporting instances applied to a PUCCH based CSI report in LTE (e.g., transmitted in an order of RI, WB PMI/CQI, SB PMI/CQI) is not applied. Instead, in NR, there is a limit that a specific CSI report is not configured in a short/long PUCCH and a CSI omission rule is defined. In addition, regarding AP CSI reporting timing, a PUSCH symbol/slot location is dynamically indicated by DCI. In addition, candidate slot offsets are configured by RRC. For CSI reporting, a slot offset (Y) is configured per reporting setting. For UL-SCH, a slot offset K2 is separately configured. [00334]2 CSI latency classes (low latency class, high latency class) are defined with regard to CSI computation complexity. Low latency CSI is WB CSI which includes up to 4 ports Type-I codebooks or up to 4 ports non-PMI feedback CSI. High latency CSI refers to CSI other than low latency CSI. For a normal terminal, (Z, Z′) is defined in a unit of OFDM symbols. Here, Z represents the minimum CSI processing time until a CSI report is performed after receiving aperiodic CSI triggering DCI. In addition, Z′ refers to the minimum CSI processing time until a CSI report is performed after receiving a CSI-RS for a channel/interference.
Additionally, a terminal reports the number of CSI which may be calculated at the same time.
In order to perform the above-described CSI-related operations, a CSI reference resource may mean a frequency and time unit (i.e., resource) that is assumed to be allocated/transmitted by a PDSCH when a UE calculates/derives CSI, and may be defined as follows.
The CSI reference resource for a serving cell is defined as follows:
In the frequency domain, the CSI reference resource is defined by the group of downlink physical resource blocks corresponding to the band to which the derived CSI relates.
Here, Koffset is a parameter set by the higher layer as specified in Clause 4.2 of TS 38.213. And, μKoffset is a subcarrier spacing (SCS) setting for Koffset with a value of 0 for FR 1.
Here,
and μDL and μUL are the subcarrier spacing configurations for DL and UL, respectively. Nslot,offsetCA and μoffset are determined by the ca-SlotOffset configured by the higher layer for cells transmitting uplink and downlink as defined in Clause 4.5 of TS 38.211.
Here, for periodic and semi-persistent CSI report, i) if a single CSI-RS/SSB resource is configured for channel measurement, nCSI_ref is the smallest value greater than or equal to 4·2μDL, such that it corresponds to a valid downlink slot, or ii) if multiple CSI-RS/SSB resources are configured for channel measurement, nCSI_ref is the smallest value greater than or equal to 5·2μDL, such that it corresponds to a valid downlink slot.
In addition, for aperiodic CSI reporting, of the UE is indicated by the DCI to report CSI in the same slot as the CSI request, nCSI_ref is such that the reference resource is in the same valid downlink slot as the corresponding CSI request. Otherwise nCSI_ref is the smallest value greater than or equal to └Z′/Nsymbslot┘, such that slot n-nCSI_ref corresponds to a valid downlink slot, where Z′ corresponds to the delay requirement as defined in Clause 5.4 of TS 38.214.
When periodic or semi-persistent CSI-RS/CSI-IM or SSB is used for channel/interference measurements, the UE is not expected to measure channel/interference on the CSI-RS/CSI-IM/SSB whose last OFDM symbol is received up to Z′ symbols before transmission time of the first OFDM symbol of the aperiodic CSI reporting.
In the above description, a slot of a serving cell is considered a valid downlink slot if i) it is a slot that includes at least one downlink or flexible symbol configured by a higher layer and ii) it is not within a configured measurement gap for the UE.
In addition, a CQI is defined as follows based on the above-described CSI reference resource, and channel measurement resources (CMR) and/or interference measurement resources (IMR) used for CQI calculation may be restricted as follows depending on measurement restriction (MR) (i.e., depending on whether MR is set).
The CQI indices and their interpretations are given in Table 5.2.2.1-2 or Table 5.2.2.1-4 of Clause 5.2.2.1 of TS 38.214 for reporting CQI based on QPSK, 16QAM and 64QAM. The CQI indices and their interpretations are given in Table 5.2.2.1-3 of Clause 5.2.2.1 of TS 38.214 for reporting CQI based on QPSK, 16QAM, 64QAM and 256QAM. The CQI indices and their interpretations are given in Table 5.2.2.1-5 of Clause 5.2.2.1 of TS 38.214 for reporting CQI based on QPSK, 16QAM, 64QAM, 256QAM and 1024 QAM.
Based on an unrestricted observation interval in time unless specified otherwise, and an unrestricted observation interval in frequency, the UE shall derive for each CQI value reported in uplink slot n the highest CQI index which satisfies the following condition:
A single PDSCH transport block with a combination of modulation scheme, target code rate and transport block size corresponding to the CQI index, and occupying a group of downlink physical resource blocks termed the CSI reference resource, could be received with a transport block error probability not exceeding:
If the higher layer parameter timeRestrictionForChannelMeasurements is set to “notConfigured” (i.e., if no time limit is configured for channel measurement), the UE shall derive the channel measurements for computing CSI value reported in uplink slot n based on only the NZP CSI-RS, no later than the CSI reference resource, associated with the CSI resource setting.
If the higher layer parameter timeRestrictionForChannelMeasurements in CSI-ReportConfig is set to “Configured” (i.e., if time limit is configured for channel measurement), the UE shall derive the channel measurements for computing CSI reported in uplink slot n based on only the most recent, no later than the CSI reference resource, occasion of NZP CSI-RS associated with the CSI resource setting.
If the higher layer parameter timeRestrictionForInterferenceMeasurements is set to “notConfigured” (i.e., if no time limit is configured for interference measurement), the UE shall derive the interference measurements for computing CSI value reported in uplink slot n based on only the CSI-IM and/or NZP CSI-RS for interference measurement no later than the CSI reference resource associated with the CSI resource setting.
If the higher layer parameter timeRestrictionForInterferenceMeasurements in CSI-ReportConfig is set to “Configured” (i.e., if time limit is configured for interference measurement), the UE shall derive the interference measurements for computing the CSI value reported in uplink slot n based on the most recent, no later than the CSI reference resource, occasion of CSI-IM and/or NZP CSI-RS for interference measurement associated with the CSI resource setting.
Hereinafter, the embodiments/methods proposed in the present disclosure can be applied to a UE (or, for convenience, referred to as an AI UE) having a function/capability (e.g., see
Problem 1: Since a CSI reference resource is configured to a CSI reporting time or a time before that, there is a difference between the time when CSI is calculated and the time when a UE actually receives a PDSCH, making it vulnerable to channel aging.
Referring to
Embodiment 1: In order to improve the above-described problem 1, a method of configuring a CSI reference resource to the time at which CSI is reported or after the time at which CSI is reported is proposed.
Referring to
In order to configure a CSI reference resource at or after a CSI reporting time, for example, the variable nCSI_ref for determining the CSI reference resource can be determined as the minimum value among values greater than or equal to a specific value (hereinafter, M) such that slot n-nCSI_ref (or slot
corresponds to a valid downlink slot.
Here, unlike the existing standard, M can be negative, and when a value of M is negative, the proposed CSI reference resource (1703) as in
As described above, a slot of a serving cell may be considered a valid downlink slot if i) it includes at least one downlink or flexible symbol configured by a higher layer and ii) it is not included in a configured measurement gap for a UE.
The higher the channel prediction capability of an AI UE, the smaller the M value can be supported (i.e., a smaller negative value), and by applying a smaller M value, a CSI reference resource can be configured further into the future than a CSI reporting time.
A UE may report to a base station candidates (and/or range) of M values and channel prediction accuracy for each candidate (and/or M values belonging to the range) (if a range is used, a unit of available values for M values may be predefined). A base station may determine the M value and configure it to the UE based on the channel prediction accuracy for each candidate (and/or M values within the range) received from the UE. Alternatively, the UE may determine M value and transmit the M value and channel prediction accuracy thereof to a base station together with CSI.
In addition, a base station may indicate candidates (and/or range) of M value to a UE, and the UE may report the candidate prediction accuracy for each candidate (and/or M value belonging to the range) to the base station. In this case, the base station may determine the M value based on the channel prediction accuracy for each candidate (and/or M value belonging to the range) received from the UE and configure it to the UE.
In addition, a base station can configure/indicate a threshold for channel prediction accuracy, and a UE can report to the base station the M value that exceeds the threshold. For example, a UE can report to a base station only the maximum M value that exceeds a threshold, or can report to a base station one or more M values that exceed a threshold. In addition, when reporting to a base station one or more M values that exceed a threshold, a UE can also report to the base station the channel prediction accuracy for each M value. If more than one M value is reported to a base station, the base station can determine the M value based on the channel prediction accuracy for each M value and configure it to a UE.
In addition, (in addition to the above-described operations), a UE may report to a base station information on a measurement window required to achieve channel prediction accuracy for a specific M. For example, a UE may report to a base station that it needs w2 CMRs and/or IMRs during w1 slots during which it performs channel measurements for CSI calculation. In this case, a base station may ensure this (i.e., configure CMRs and/or IMRs so that w2 CMRs and/or IMRs exist during w1 slots) so that a UE can achieve channel prediction accuracy for a specific M.
In the above description, the channel prediction means that a UE predicts a channel of a CSI reference resource based on a CMR used for measurement. In addition, the prediction accuracy for the channel prediction means accuracy predicted by a UE, not the accuracy between an actual channel and a predicted channel in a CSI reference resource. In other words, even if a UE reports prediction accuracy as 100%, an actual channel and a predicted channel may be different.
In addition, a UE can calculate CSI for each of multiple CSI reference resources for multiple M values. For example, a UE can calculate CSI for a CSI reference resource configured/determined based on M=−2, and for a CSI reference resource configured/determined based on M=−5, and the UE can transmit both CSIs to a base station. In this case, CSI feedback overhead can be reduced by transmitting a difference value for the remaining CSI based on a specific CSI among the two CSIs (e.g., CSI for a larger (or smaller) M). Alternatively, a UE can transmit CSI calculated for the existing CSI reference resource (i.e., a CSI reference resource configured before a CSI reporting time) together with M CSIs for one or more M values, and in this case, only the difference value for CSI calculated for the existing reference resource can be transmitted to a base station.
In the case of aperiodic (AP) CSI reporting, a base station can indicate an M value and/or a threshold for channel prediction accuracy through triggering DCI. For example, if a single M value is indicated, a CSI reference resource can be determined using the corresponding M value. Alternatively, if a threshold for channel prediction accuracy is indicated, a CSI reference resource can be determined using an M value that exceeds a threshold. If there are multiple M values that exceed a threshold, a CSI reference resource can be determined using an M value with the maximum channel prediction accuracy.
Meanwhile, a CSI reference resource proposed in the above-described Embodiment 1 was calculated based on slot n′ as a CSI reporting time, however the proposed CSI reference resource can be determined based on the existing CSI reference resource (i.e., a CSI reference resource configured before a CSI reporting time). For example, assuming that the existing CSI reference resource is slot R, the proposed CSI reference resources can be determined as the most recent valid downlink slot in a slot less than or equal to slot R+R′ (R′ is a natural number).
In addition, a UE can additionally report to a base station how long CSI is valid along with CSI. That is, a UE can report to a base station time information applicable to CSI. For example, a UE can report to a base station that the corresponding CSI is valid for K slots (for example, K=2, where K is a natural number) starting from the CSI reporting slot n′. That is, through this, a UE can inform a base station that CSI accurately reflects the state of the DL channel during slot n′, slot n′+1, and slot n′+2, so that a base station can perform DL transmission (for example, PDSCH) using the corresponding CSI, and after slot n′+2, the DL channel changes, so that the DL performance deteriorates when the corresponding CSI (i.e., CSI reported in the CSI reporting slot n′) is used for DL transmission. For example, if a CMR/IMR is a periodic/semi-persistent CSI-RS, a UE can report a measurement window to a base station according to a RS period.
Problem 2: When using the proposed CSI reference resource according to Embodiment 1, a problem may occur in existing measurement operations from a CMR and/or IMR.
In this case, a problem may occur in the existing measurement operation (from CMR or IMR (1802)) when using the CSI reference resource (1804) proposed in Embodiment 1. More specifically, according to the existing measurement operation, when measurement restriction (MR) is ON/enabled, a UE is restricted to perform measurement using the most recently configured CMR in the CSI reference resource slot or the previous time, and when the MR is OFF/deactivated, the UE can perform measurement using multiple CMRs in the reference resource slot or the previous time. Therefore, according to the existing measurement operation, the UE must predict the channel of the CSI reference resource proposed in Embodiment 1 from the channel estimated using these CMRs.
However, since the CSI reference resource (1804) proposed in Embodiment 1 is configured/determined after the CSI report (1803) time, if the existing MR configuration is applied, it is defined to use CMR 4,5 configured at or after the CSI report (1803) time for measurement. In this case, since the UE cannot use the CMR configured at or after the CSI report (1803) time, a problem occurs in that such measurement is no longer valid.
Embodiment 2: In order to solve the above-described problem 2, a method is proposed to restrict the use of CMR (or IMR) prior to CSI reporting time for measurement.
More specifically, when MR (measurement restriction) is ON, the most recently configured CMR (or IMR) is restricted to be used at a time prior to CSI reporting, and when MR is OFF, multiple CMRs (or IMR) can be used at a time prior to CSI reporting. Referring to
Here, if CSI reporting and a CMR are configured too close to each other, a UE's processing time (e.g., CSI processing time z′ defined in the NR standard, where Z′ represents the minimum CSI processing time from receiving CSI-RS (i.e., CMR/IMR) for channel/interference to performing CSI reporting.) is insufficient to measure/calculate and report CSI from the corresponding CMR. For example, in
That is, in Embodiment 2, instead of defining MR based on the CSI reporting time, MR can be defined based on the {CSI reporting time—offset} value. In other words, CMR (or IMR) prior to {CSI reporting time—offset} can be restricted to be used for measurement. Accordingly, CMR (or IMR) 3 of
Alternatively, in order to solve the above-mentioned problem 2, MR can be defined based on the existing CSI reference resource (i.e., CSI reference resource set before the CSI reporting time). That is, when MR is ON, it is limited to using the most recently configured CMR from the existing CSI reference resource slot or the previous time, and when MR is OFF, multiple CMRs can be used from the existing CSI reference resource slot or the previous time. Here, of course, the existing CSI reference resource is only used for defining MR, and ab actual CSI reference resource can be used as the proposed CSI reference resource. Accordingly, in
Problem 3: In order to accurately estimate the channel of the proposed CSI reference resource according to Embodiment 1, extrapolation from multiple CMRs measured at different times is required. Therefore, the existing MR ON operation that uses only one CMR may be accurate or inaccurate depending on an AI/ML capability of a UE. In addition, the number of CMRs required for prediction may vary depending on the AI/ML capability of the UE.
Embodiment 3: To solve the above problem 3, MR ON can be defined in several levels.
Here, a UE can report these MR levels (and/or prediction accuracy according to each MR level) to a base station.
Alternatively, a base station can indicate an MR level to a UE or request the prediction accuracy according to an MR level. Alternatively, a base station can configure a UE to report an MR level whose prediction accuracy exceeds a threshold.
For example, MR levels can be defined as follows:
For example, in case of using MR level 0 based on the existing CSI reference resource (1801) in
A UE can calculate CSI for each of multiple MR levels and report multiple CSIs calculated in this way to a base station. In this case, the feedback overhead can be reduced by reporting a difference value of the remaining CSI based on CSI measured using a specific MR level (e.g., MR level 0).
A UE may report/request to a base station that a time interval between the most recently received CMR before the proposed CSI reference resource and the proposed CSI reference resource is less than a certain value (e.g., T1) to secure prediction accuracy, and/or may report/request to a base station that a time interval between P/SP CMRs is less than a certain value (e.g., T2). Here, if these time conditions are not met, a UE may not report the corresponding CSI or may report the most recently reported CSI without calculation.
In the above-described Embodiments 1 to 3, when a UE reports multiple CSIs, a method of reporting a difference value based on specific CSI as described above may be used, and/or some CSI (i.e., some information/indications within CSI) may be assumed as common CSI and reported to a base station only once without duplication. For example, since an RI may not change significantly depending on a CSI reference resource, a UE may report only one common value for an RI to abase station, and may calculate and report the remaining PMI/CQI, etc. to the base station respectively according to the CSI reference resource.
In addition, in the above-described Embodiments 1 to 3, for the convenience of explanation, the explanation was mainly focused on a CMR, but the same method can be applied to an IMR as well.
In addition, any one of the methods suggested in the above-described Embodiments 1 to 3 may be applied, and multiple methods among the proposed methods may be combined/aggregated and finally applied.
In addition, whether or not to apply the methods suggested in the above-described Embodiments 1 to 3 can be indicated by a base station to a UE through signaling such as RRC/MAC-CE/DCI (for example, DCI activating SP CSI reporting or DCI triggering AP CSI reporting, etc.).
The signaling method described in
A base station may be a general term for an object that performs data transmission and reception with a UE. For example, a base station may be a concept including one or more TPs (Transmission Points), one or more TRPs (Transmission and Reception Points), etc. In addition, a TP and/or TRP may include a panel, a transmission and reception unit, etc. of a base station. In addition, “TRP” may be applied by substituting it with expressions such as panel, antenna array, cell (e.g., macro cell/small cell/pico cell, etc.), transmission point, base station (gNB, etc.). As described above, TRPs can be distinguished based on information (e.g., index, ID) about the CORESET group (or CORESET pool). As described above, TRPs can be distinguished based on information (e.g., index, ID) on a CORESET group (or CORESET pool). For example, if one UE is configured to perform transmission and reception with multiple TRPs (or cells), this may mean that multiple CORESET groups (or CORESET pools) are configured for one UE. Such configuration of CORESET groups (or CORESET pools) can be performed via higher layer signaling (e.g., RRC signaling, etc.).
A UE can receive configuration information from a network (S1901).
The configuration information may include information related to network configuration (e.g., TRP configuration)/information related to M-TRP-based transmission and reception (e.g., resource allocation, etc.). Here, the configuration information may be transmitted through higher layer signaling (e.g., RRC signaling, MAC-CE, etc.).
The configuration information may include configuration information related to CSI described in the above-described proposed methods (e.g., Embodiments 1 to 3, a combination of one or more of the proposed methods in Embodiments 1 to 3).
The configuration information related to CSI may include at least one of information related to CSI-IM (interference management) resource, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.
Here, the configuration information related to CSI may include configuration information related to CSI reporting (i.e., CSI report configuration) (e.g., RRC IE ‘CSI-ReportConfig’) (hereinafter, first configuration information) and configuration information related to the CSI resource (i.e., CSI resource configuration) (e.g., RRC IE ‘CSI-ResourceConfig’) (hereinafter, second configuration information).
Here, multiple CSI-RS resources for CSI calculation/measurement can be configured for a UE by configuration information related to CSI resources (i.e., CSI resource settings). Additionally, one or more channel measurement resources (CMR) (e.g., NZP CSI-RS) and/or one or more interference measurement resources (IMR) (e.g., CSI-IM and/or NZP CSI-RS) may be configured by configuration information related to the CSI resource (i.e., CSI resource settings). CMR and IMR may be collectively referred to as CSI resources.
In addition, the configuration information related to the CSI resource (i.e., the CSI resource setting) can be connected/associated with the configuration information related to a specific CSI report (i.e., the CSI report setting), and according to the report quantity (reportQuantity) in the CSI report setting, a CSI report (e.g., CRI, PMI, RI, CQI, LI, etc.) for multiple CSI resources configured by the connected/associated CSI resource setting can be configured for a UE.
A UE receives a CSI-RS from multiple CSI resources configured by configuration information (e.g., CSI resource setting) from a network (S1902).
A UE can receive downlink control information (DCI) from a network (S1903).
Here, DCI may be DCI that triggers aperiodic CSI reporting to a UE, or may be DCI that activates semi-persistent CSI reporting.
In addition, DCI may be transmitted via a downlink control channel (e.g., PDCCH, etc.), and may schedule a downlink data channel (e.g., PDSCH)/uplink data channel (e.g., PUSCH) in addition to triggering/activating CSI reporting.
A UE transmits (reports) CSI to a network (S1904).
Here, CSI may include at least one of a CQI, a PMI, an RI, a CRI, a LI.
As described above, a UE may report CSI to a network based on the configuration information related to CSI reporting (and according to trigger/activation by DCI). For example, the configuration information related to CSI reporting may configure CSI reporting types such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting, and may configure what information (e.g., CRI, CQI, PMI, RI, LI, etc.) should be reported in the CSI, and in case of periodic CSI reporting, periodicity and slot offset, etc. may also be configured.
According to the Embodiments 1 to 3 described above, CSI can be calculated/obtained by deriving/performing channel measurement and/or interference measurement based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource.
Here, according to the above-described Embodiment 1, a CSI reference resource may be determined as a slot later than a slot in which the CSI is reported in a time domain. For this purpose, for example, the CSI reference resource may be determined based on slot n-nCSI_ref later than the uplink slot n′ in which the CSI is reported, and the nCSI_ref may be determined as a minimum value greater than or equal to the variable (M) such that the CSI reference resource corresponds to a valid downlink slot.
Here, a value of the variable (M) may be provided by the configuration information (e.g., configuration information related to CSI reporting) or downlink control information (DCI) that triggers reporting of the CSI. Here, a UE can transmit/report, to a network, candidates of the variable (M) and the channel prediction accuracy (i.e., a UE calculates the channel prediction accuracy assuming a CSI reference resource determined by applying the variable (M)) for each slot according to the candidates of the variable (M). In this case, a network can also determine the variable (M) based on this and provide it to a UE.
In addition, a UE may determine the variable (M) and transmit/report the variable (M) and the channel prediction accuracy for a slot according to the variable (M) to a network. For example, the variable (M) and the channel prediction accuracy for a slot according to the variable (M) may be transmitted to a network together with (or included in) CSI.
In addition, a threshold is configured by a network (or a threshold is determined/defined in advance), and a UE can transmit/report to the network a variable (M) for a slot where the channel prediction accuracy exceeds the threshold. For example, the variable (M) can be transmitted to the network together with (or included in) CSI.
In addition, although not shown in
In addition, the CSI may include N CSIs for each of N CSI reference resources according to N variables (M) (N is a natural number). For example, CSI finally reported to a network may include both first CSI for a first CSI reference resource determined by applying a first variable (M1) and second CSI for a second CSI reference resource determined by applying a second variable (M2). In this case, the CSI may also include the N variables (M).
In addition, the CSI may include information on a time when the corresponding CSI is valid.
In addition, according to the above-described Embodiment 2, channel measurement and/or interference measurement may be derived/performed based on the one or more CMRs and/or the one or more IMRs prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported in order to calculate the CSI. That is, the one or more CMRs and/or the one or more IMRs used to calculate the CSI may be limited to prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported.
In addition, according to the above-described Embodiment 3, measurement restriction (MR) can be defined in multiple levels. In this case, the number of CMRs and/or IMRs used to calculate the CSI can be determined according to a level of the measurement restriction.
Meanwhile, the operation of calculating the CSI (i.e., an operation of calculating the CSI by predicting the channel) (or an operation of calculating the channel prediction accuracy) described above can be performed by the UE (or an external device mounted or connected to the UE), and at least one of the functions for the AI/ML operation of the above-described
Referring to
In addition, an operation of
A UE receives configuration information related to channel state information (CSI) from a base station (S2001).
The configuration information related to CSI may include configuration information related to CSI described in the above-described proposed methods (e.g., Embodiments 1 to 3, a combination of one or more of the proposed methods in Embodiments 1 to 3).
The configuration information related to CSI may include at least one of information related to CSI-IM (interference management) resource, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.
Here, the configuration information related to CSI may include configuration information related to CSI reporting (i.e., CSI report configuration) (e.g., RRC IE ‘CSI-ReportConfig’) (hereinafter, first configuration information) and configuration information related to the CSI resource (i.e., CSI resource configuration) (e.g., RRC IE ‘CSI-ResourceConfig’) (hereinafter, second configuration information).
Here, multiple CSI-RS resources for CSI calculation/measurement can be configured for a UE by configuration information related to CSI resources (i.e., CSI resource settings). Additionally, one or more channel measurement resources (CMR) (e.g., NZP CSI-RS) and/or one or more interference measurement resources (IMR) (e.g., CSI-IM and/or NZP CSI-RS) may be configured by configuration information related to the CSI resource (i.e., CSI resource settings). CMR and IMR may be collectively referred to as CSI resources.
In addition, the configuration information related to the CSI resource (i.e., the CSI resource setting) can be connected/associated with the configuration information related to a specific CSI report (i.e., the CSI report setting), and according to the report quantity (reportQuantity) in the CSI report setting, a CSI report (e.g., CRI, PMI, RI, CQI, LI, etc.) for multiple CSI resources configured by the connected/associated CSI resource setting can be configured for a UE.
Although not shown in
A UE calculates/obtains CSI by deriving/performing channel measurements and/or interference measurements based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource (S2002). In addition, a UE transmits (reports) CSI to a network (S2003). Here, CSI may include at least one of a CQI, a PMI, an RI, a CRI, a LI.
That is, a UE receives a CSI-RS from multiple CSI resources configured by configuration information (e.g., CSI resource settings), and calculates CSI based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource. In addition, a UE may report CSI to a network based on the configuration information related to CSI reporting (and according to trigger/activation by DCI). For example, the configuration information related to CSI reporting may configure CSI reporting types such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting, and may configure what information (e.g., CRI, CQI, PMI, RI, LI, etc.) should be reported in the CSI, and in case of periodic CSI reporting, periodicity and slot offset, etc. may also be configured.
According to the Embodiments 1 to 3 described above, CSI can be calculated/obtained by deriving/performing channel measurement and/or interference measurement based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource.
Here, according to the above-described Embodiment 1, a CSI reference resource may be determined as a slot later than a slot in which the CSI is reported in a time domain. For this purpose, for example, the CSI reference resource may be determined based on slot n-nCSI_ref later than the uplink slot n′ in which the CSI is reported, and the nCSI_ref may be determined as a minimum value greater than or equal to the variable (M) such that the CSI reference resource corresponds to a valid downlink slot.
Here, a value of the variable (M) may be provided by the configuration information (e.g., configuration information related to CSI reporting) or downlink control information (DCI) that triggers reporting of the CSI. Here, a UE can transmit/report, to a base station, candidates of the variable (M) and the channel prediction accuracy (i.e., a UE calculates the channel prediction accuracy assuming a CSI reference resource determined by applying the variable (M)) for each slot according to the candidates of the variable (M). In this case, a base station can also determine the variable (M) based on this and provide it to a UE.
In addition, a UE may determine the variable (M) and transmit/report the variable (M) and the channel prediction accuracy for a slot according to the variable (M) to a base station. For example, the variable (M) and the channel prediction accuracy for a slot according to the variable (M) may be transmitted, to a base station, together with (or included in) CSI.
In addition, a threshold is configured by a base station (or a threshold is determined/defined in advance), and a UE can transmit/report to the base station a variable (M) for a slot where the channel prediction accuracy exceeds the threshold. For example, the variable (M) can be transmitted to a base station together with (or included in) CSI.
In addition, although not shown in
In addition, the CSI may include N CSIs for each of N CSI reference resources according to N variables (M) (N is a natural number). For example, CSI finally reported to a network may include both first CSI for a first CSI reference resource determined by applying a first variable (M1) and second CSI for a second CSI reference resource determined by applying a second variable (M2). In this case, the CSI may also include the N variables (M).
In addition, the CSI may include information on a time when the corresponding CSI is valid.
In addition, according to the above-described Embodiment 2, channel measurement and/or interference measurement may be derived/performed based on the one or more CMRs and/or the one or more IMRs prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported in order to calculate the CSI. That is, the one or more CMRs and/or the one or more IMRs used to calculate the CSI may be limited to prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported.
In addition, according to the above-described Embodiment 3, measurement restriction (MR) can be defined in multiple levels. In this case, the number of CMRs and/or IMRs used to calculate the CSI can be determined according to a level of the measurement restriction.
Meanwhile, the operation of calculating the CSI (i.e., an operation of calculating the CSI by predicting the channel) (or an operation of calculating the channel prediction accuracy) described above can be performed by the UE (or an external device mounted or connected to the UE), and at least one of the functions for the AI/ML operation of the above-described
Referring to
In addition, an operation of
A base station transmits configuration information related to channel state information (CSI) to a UE (S2101).
The configuration information related to CSI may include configuration information related to CSI described in the above-described proposed methods (e.g., Embodiments 1 to 3, a combination of one or more of the proposed methods in Embodiments 1 to 3).
The configuration information related to CSI may include at least one of information related to CSI-IM (interference management) resource, information related to CSI measurement configuration, information related to CSI resource configuration, or information related to CSI report configuration.
Here, the configuration information related to CSI may include configuration information related to CSI reporting (i.e., CSI report configuration) (e.g., RRC IE ‘CSI-ReportConfig’) (hereinafter, first configuration information) and configuration information related to the CSI resource (i.e., CSI resource configuration) (e.g., RRC IE ‘CSI-ResourceConfig’) (hereinafter, second configuration information).
Here, multiple CSI-RS resources for CSI calculation/measurement can be configured for a UE by configuration information related to CSI resources (i.e., CSI resource settings). Additionally, one or more channel measurement resources (CMR) (e.g., NZP CSI-RS) and/or one or more interference measurement resources (IMR) (e.g., CSI-IM and/or NZP CSI-RS) may be configured by configuration information related to the CSI resource (i.e., CSI resource settings). CMR and IMR may be collectively referred to as CSI resources.
In addition, the configuration information related to the CSI resource (i.e., the CSI resource setting) can be connected/associated with the configuration information related to a specific CSI report (i.e., the CSI report setting), and according to the report quantity (reportQuantity) in the CSI report setting, a CSI report (e.g., CRI, PMI, RI, CQI, LI, etc.) for multiple CSI resources configured by the connected/associated CSI resource setting can be configured for a UE.
Although not shown in
A base station receives CSI from a UE (S2102). Here, CSI may include at least one of a CQI, a PMI, an RI, a CRI, a LI.
Here, the CSI is calculated/obtained by deriving/performing channel measurements and/or interference measurements based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource.
That is, a CSI-RS is transmitted to a UE from multiple CSI resources configured by configuration information (e.g., CSI resource setting), and CSI is calculated based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource. In addition, a base station can receive CSI from a UE based on the configuration information related to CSI reporting (and according to trigger/activation by DCI). For example, according to the configuration information related to CSI reporting, CSI reporting types such as periodic CSI reporting, semi-persistent CSI reporting, and aperiodic CSI reporting can be configured, and what information (e.g., CRI, CQI, PMI, RI, LI, etc.) should be reported in CSI can be configured, and in the case of periodic CSI reporting, periodicity and slot offset, etc. can also be configured.
According to the Embodiments 1 to 3 described above, CSI can be calculated/obtained by deriving/performing channel measurement and/or interference measurement based on one or more CSI resources (i.e., one or more CMRs and/or one or more IMRs) determined based on a CSI reference resource.
Here, according to the above-described Embodiment 1, a CSI reference resource may be determined as a slot later than a slot in which the CSI is reported in a time domain. For this purpose, for example, the CSI reference resource may be determined based on slot n-nCSI_ref later than the uplink slot n′ in which the CSI is reported, and the nCSI_ref may be determined as a minimum value greater than or equal to the variable (M) such that the CSI reference resource corresponds to a valid downlink slot.
Here, a value of the variable (M) may be provided by the configuration information (e.g., configuration information related to CSI reporting) or downlink control information (DCI) that triggers reporting of the CSI. Here, a UE can transmit/report, to a base station, candidates of the variable (M) and the channel prediction accuracy (i.e., a UE calculates the channel prediction accuracy assuming a CSI reference resource determined by applying the variable (M)) for each slot according to the candidates of the variable (M). In this case, a base station can also determine the variable (M) based on this and provide it to a UE.
Additionally, a base station can receive, from a UE, a variable (M) determined by the UE and the channel prediction accuracy for a slot according to the variable (M). For example, the variable (M) and the channel prediction accuracy for a slot according to the variable (M) may be received, from a UE, together with (or included in) CSI.
Additionally, a variable (M) for a slot in which the channel prediction accuracy exceeds a threshold configured by a base station (or a pre-determined/defined threshold) can be received from a UE. For example, the variable (M) can be received from a UE together with (or included in) CSI.
In addition, although not shown in
In addition, the CSI may include N CSIs for each of N CSI reference resources according to N variables (M) (N is a natural number). For example, CSI finally reported to a network may include both first CSI for a first CSI reference resource determined by applying a first variable (M1) and second CSI for a second CSI reference resource determined by applying a second variable (M2). In this case, the CSI may also include the N variables (M).
In addition, the CSI may include information on a time when the corresponding CSI is valid.
In addition, according to the above-described Embodiment 2, channel measurement and/or interference measurement may be derived/performed based on the one or more CMRs and/or the one or more IMRs prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported in order to calculate the CSI. That is, the one or more CMRs and/or the one or more IMRs used to calculate the CSI may be limited to prior to a uplink slot in which the CSI is reported or prior to an offset from a uplink slot in which the CSI is reported.
In addition, according to the above-described Embodiment 3, measurement restriction (MR) can be defined in multiple levels. In this case, the number of CMRs and/or IMRs used to calculate the CSI can be determined according to a level of the measurement restriction.
Meanwhile, the operation of calculating the CSI (i.e., an operation of calculating the CSI by predicting the channel) (or an operation of calculating the channel prediction accuracy) described above can be performed by the UE (or an external device mounted or connected to the UE), and at least one of the functions for the AI/ML operation of the above-described
General Device to which the Present Disclosure May be Applied
In reference to
A first wireless device 100 may include one or more processors 102 and one or more memories 104 and may additionally include one or more transceivers 106 and/or one or more antennas 108. A processor 102 may control a memory 104 and/or a transceiver 106 and may be configured to implement description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure. For example, a processor 102 may transmit a wireless signal including first information/signal through a transceiver 106 after generating first information/signal by processing information in a memory 104. In addition, a processor 102 may receive a wireless signal including second information/signal through a transceiver 106 and then store information obtained by signal processing of second information/signal in a memory 104. A memory 104 may be connected to a processor 102 and may store a variety of information related to an operation of a processor 102. For example, a memory 104 may store a software code including commands for performing all or part of processes controlled by a processor 102 or for performing description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure. Here, a processor 102 and a memory 104 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (e.g., LTE, NR). A transceiver 106 may be connected to a processor 102 and may transmit and/or receive a wireless signal through one or more antennas 108. A transceiver 106 may include a transmitter and/or a receiver. A transceiver 106 may be used together with a RF (Radio Frequency) unit. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.
A second wireless device 200 may include one or more processors 202 and one or more memories 204 and may additionally include one or more transceivers 206 and/or one or more antennas 208. A processor 202 may control a memory 204 and/or a transceiver 206 and may be configured to implement description, functions, procedures, proposals, methods and/or operation flows charts disclosed in the present disclosure. For example, a processor 202 may generate third information/signal by processing information in a memory 204, and then transmit a wireless signal including third information/signal through a transceiver 206. In addition, a processor 202 may receive a wireless signal including fourth information/signal through a transceiver 206, and then store information obtained by signal processing of fourth information/signal in a memory 204. A memory 204 may be connected to a processor 202 and may store a variety of information related to an operation of a processor 202. For example, a memory 204 may store a software code including commands for performing all or part of processes controlled by a processor 202 or for performing description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure. Here, a processor 202 and a memory 204 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (e.g., LTE, NR). A transceiver 206 may be connected to a processor 202 and may transmit and/or receive a wireless signal through one or more antennas 208. A transceiver 206 may include a transmitter and/or a receiver. A transceiver 206 may be used together with a RF unit. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.
Hereinafter, a hardware element of a wireless device 100, 200 will be described in more detail. It is not limited thereto, but one or more protocol layers may be implemented by one or more processors 102, 202. For example, one or more processors 102, 202 may implement one or more layers (e.g., a functional layer such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors 102, 202 may generate one or more PDUs (Protocol Data Unit) and/or one or more SDUs (Service Data Unit) according to description, functions, procedures, proposals, methods and/or operation flow charts included in the present disclosure. One or more processors 102, 202 may generate a message, control information, data or information according to description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure. One or more processors 102, 202 may generate a signal (e.g., a baseband signal) including a PDU, a SDU, a message, control information, data or information according to functions, procedures, proposals and/or methods disclosed in the present disclosure to provide it to one or more transceivers 106, 206. One or more processors 102, 202 may receive a signal (e.g., a baseband signal) from one or more transceivers 106, 206 and obtain a PDU, a SDU, a message, control information, data or information according to description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure.
One or more processors 102, 202 may be referred to as a controller, a micro controller, a micro processor or a micro computer. One or more processors 102, 202 may be implemented by a hardware, a firmware, a software, or their combination. In an example, one or more ASICs (Application Specific Integrated Circuit), one or more DSPs (Digital Signal Processor), one or more DSPDs (Digital Signal Processing Device), one or more PLDs (Programmable Logic Device) or one or more FPGAs (Field Programmable Gate Arrays) may be included in one or more processors 102, 202. Description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure may be implemented by using a firmware or a software and a firmware or a software may be implemented to include a module, a procedure, a function, etc. A firmware or a software configured to perform description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure may be included in one or more processors 102, 202 or may be stored in one or more memories 104, 204 and driven by one or more processors 102, 202. Description, functions, procedures, proposals, methods and/or operation flow charts disclosed in the present disclosure may be implemented by using a firmware or a software in a form of a code, a command and/or a set of commands.
One or more memories 104, 204 may be connected to one or more processors 102, 202 and may store data, a signal, a message, information, a program, a code, an instruction and/or a command in various forms. One or more memories 104, 204 may be configured with ROM, RAM, EPROM, a flash memory, a hard drive, a register, a cash memory, a computer readable storage medium and/or their combination. One or more memories 104, 204 may be positioned inside and/or outside one or more processors 102, 202. In addition, one or more memories 104, 204 may be connected to one or more processors 102, 202 through a variety of technologies such as a wire or wireless connection.
One or more transceivers 106, 206 may transmit user data, control information, a wireless signal/channel, etc. mentioned in methods and/or operation flow charts, etc. of the present disclosure to one or more other devices. One or more transceivers 106, 206 may receiver user data, control information, a wireless signal/channel, etc. mentioned in description, functions, procedures, proposals, methods and/or operation flow charts, etc. disclosed in the present disclosure from one or more other devices. For example, one or more transceivers 106, 206 may be connected to one or more processors 102, 202 and may transmit and receive a wireless signal. For example, one or more processors 102, 202 may control one or more transceivers 106, 206 to transmit user data, control information or a wireless signal to one or more other devices. In addition, one or more processors 102, 202 may control one or more transceivers 106, 206 to receive user data, control information or a wireless signal from one or more other devices. In addition, one or more transceivers 106, 206 may be connected to one or more antennas 108, 208 and one or more transceivers 106, 206 may be configured to transmit and receive user data, control information, a wireless signal/channel, etc. mentioned in description, functions, procedures, proposals, methods and/or operation flow charts, etc. disclosed in the present disclosure through one or more antennas 108, 208. In the present disclosure, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., an antenna port). One or more transceivers 106, 206 may convert a received wireless signal/channel, etc. into a baseband signal from a RF band signal to process received user data, control information, wireless signal/channel, etc. by using one or more processors 102, 202. One or more transceivers 106, 206 may convert user data, control information, a wireless signal/channel, etc. which are processed by using one or more processors 102, 202 from a baseband signal to a RF band signal. Therefor, one or more transceivers 106, 206 may include an (analogue) oscillator and/or a filter.
Embodiments described above are that elements and features of the present disclosure are combined in a predetermined form. Each element or feature should be considered to be optional unless otherwise explicitly mentioned. Each element or feature may be implemented in a form that it is not combined with other element or feature. In addition, an embodiment of the present disclosure may include combining a part of elements and/or features. An order of operations described in embodiments of the present disclosure may be changed. Some elements or features of one embodiment may be included in other embodiment or may be substituted with a corresponding element or a feature of other embodiment. It is clear that an embodiment may include combining claims without an explicit dependency relationship in claims or may be included as a new claim by amendment after application.
It is clear to a person skilled in the pertinent art that the present disclosure may be implemented in other specific form in a scope not going beyond an essential feature of the present disclosure. Accordingly, the above-described detailed description should not be restrictively construed in every aspect and should be considered to be illustrative. A scope of the present disclosure should be determined by reasonable construction of an attached claim and all changes within an equivalent scope of the present disclosure are included in a scope of the present disclosure.
A scope of the present disclosure includes software or machine-executable commands (e.g., an operating system, an application, a firmware, a program, etc.) which execute an operation according to a method of various embodiments in a device or a computer and a non-transitory computer-readable medium that such a software or a command, etc. are stored and are executable in a device or a computer. A command which may be used to program a processing system performing a feature described in the present disclosure may be stored in a storage medium or a computer-readable storage medium and a feature described in the present disclosure may be implemented by using a computer program product including such a storage medium. A storage medium may include a high-speed random-access memory such as DRAM, SRAM, DDR RAM or other random-access solid state memory device, but it is not limited thereto, and it may include a nonvolatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices or other nonvolatile solid state storage devices. A memory optionally includes one or more storage devices positioned remotely from processor(s). A memory or alternatively, nonvolatile memory device(s) in a memory include a non-transitory computer-readable storage medium. A feature described in the present disclosure may be stored in any one of machine-readable mediums to control a hardware of a processing system and may be integrated into a software and/or a firmware which allows a processing system to interact with other mechanism utilizing a result from an embodiment of the present disclosure. Such a software or a firmware may include an application code, a device driver, an operating system and an execution environment/container, but it is not limited thereto.
Here, a wireless communication technology implemented in a wireless device 100, 200 of the present disclosure may include Narrowband Internet of Things for a low-power communication as well as LTE, NR and 6G. Here, for example, an NB-IoT technology may be an example of a LPWAN (Low Power Wide Area Network) technology, may be implemented in a standard of LTE Cat NB1 and/or LTE Cat NB2, etc. and is not limited to the above-described name. Additionally or alternatively, a wireless communication technology implemented in a wireless device 100, 200 of the present disclosure may perform a communication based on a LTE-M technology. Here, in an example, a LTE-M technology may be an example of a LPWAN technology and may be referred to a variety of names such as an eMTC (enhanced Machine Type Communication), etc. For example, an LTE-M technology may be implemented in at least any one of various standards including 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) LTE M and so on and it is not limited to the above-described name. Additionally or alternatively, a wireless communication technology implemented in a wireless device 100, 200 of the present disclosure may include at least any one of a ZigBee, a Bluetooth and a low power wide area network (LPWAN) considering a low-power communication and it is not limited to the above-described name. In an example, a ZigBee technology may generate PAN (personal area networks) related to a small/low-power digital communication based on a variety of standards such as IEEE 802.15.4, etc. and may be referred to as a variety of names.
A method proposed by the present disclosure is mainly described based on an example applied to 3GPP LTE/LTE-A, 5G system, but may be applied to various wireless communication systems other than the 3GPP LTE/LTE-A, 5G system.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2022-0052403 | Apr 2022 | KR | national |
This application is the National Stage filing under 35 U.S.C. 371 of International Application No. PCT/KR2023/005650, filed on Apr. 26, 2023, which claims the benefit of earlier filing date and right of priority to Korean Application No. 10-2022-0052403, filed on Apr. 27, 2022, the contents of which are all hereby incorporated by reference herein in their entireties.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/KR2023/005650 | 4/26/2023 | WO |
