Channel state information omission in channel state information reporting

Abstract

This disclosure provides systems, apparatuses, apparatuses, and methods, including computer programs encoded on a storage medium, for CSI omission in CSI reporting. The UE 102 receives (306), from the network entity (104), a CSI-RS associated with the first CSI report. The UE (102) transmits (310) a CSI report transmission to the network entity (104), where at least a first portion of the first CSI report is omitted (308) from the CSI report transmission when a total payload size of the CSI report transmission exceeds a threshold.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to International Application No. PCT / CN2022 / 112193, filed on August 12, 2022, entitled “CSI Reports based on ML Techniques,” which is expressly incorporated herein by reference in its entirety. Technical Field

[0002] The present disclosure relates generally to wireless communications and, more particularly, to channel state information (CSI) omission in CSI reports. Background Art

[0003] The 3rd Generation Partnership Project (3GPP) specifies a radio interface called the fifth generation (5G) New Radio (NR) (5G NR). The architecture of the 5G NR wireless communication system includes a 5G core (5GC) network, a 5G radio access network (5G-RAN), user equipment (UE), etc. Compared with the previous generation cellular communication system, the 5G NR architecture seeks to provide increased data rates, reduced latency, and / or increased capacity.

[0004] Wireless communication systems generally provide various telecommunication services (e.g., telephone, video, data, messaging, broadcast, etc.) based on multiple access technologies (such as orthogonal frequency division multiple access (OFDMA) technologies) that support communication with multiple UEs. The improvement of mobile broadband continues the progress of such wireless communication technologies. For example, UEs and base stations can support more antenna configurations and multiple connections. However, one consequence is that channel state information (CSI) reports become larger and more complex. Summary of the invention

[0005] The following presents a simplified overview of one or more aspects in order to provide a basic understanding of such aspects. This overview is not an extensive review of all contemplated aspects. This overview neither identifies the key or important elements of all aspects, nor describes the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to a more detailed description presented later.

[0006] A user equipment (UE) may receive one or more channel state information-reference signals (CSI-RS) from a network entity (such as a base station) to estimate a channel based on CSI measurements of the one or more CSI-RS. The UE uses the CSI measurements to generate a channel state information (CSI) report. In an example, the UE generates at least one of the CSI reports based on a machine learning (ML) model. However, the complexity associated with ML-based CSI reporting and / or the complexity associated with the transmission of multiple CSI reports (e.g., multiple non-ML-based CSI reports or a combination of ML-based / non-ML-based CSI reports) may cause the total payload size of the CSI reports transmitted to the network entity to exceed a threshold. For example, the transmission may exceed the maximum payload size of a physical uplink shared channel (PUSCH) or physical uplink control channel (PUCCH) resource configured for the CSI report.

[0007] Aspects of the present disclosure address the above and other deficiencies by configuring a UE to omit some or all CSI reports from a CSI report transmission to reduce the total payload to a size less than or equal to the maximum payload size of a PUSCH / PUCCH resource. That is, the UE may replace a complex ML-based CSI report with a less complex non-ML-based CSI report to reduce the overhead associated with the CSI report transmission, or truncate an ML-based / non-ML-based CSI report included in a CSI report transmission to a smaller payload size.

[0008] According to some aspects, the UE receives a CSI-RS associated with a first CSI report from a network entity. The UE transmits a CSI report transmission to the network entity, wherein at least a first portion of the first CSI report is omitted from the CSI report transmission when a total payload size of the CSI report transmission exceeds a threshold.

[0009] According to some aspects, a network entity transmits a CSI-RS associated with a first CSI report to a UE. The network entity receives a CSI report transmission from the UE, wherein at least a first portion of the first CSI report is omitted from the CSI report transmission based on a total payload size of the CSI report transmission exceeding a threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A diagram showing a wireless communication system including multiple user equipments (UEs) and network entities communicating through one or more cells is shown.

[0011] Figure 2 A diagram showing an example machine learning (ML) based channel state information (CSI) encoder compression at a UE and an example ML based CSI decoder decompression at a network entity.

[0012] Figure 3A A signaling diagram illustrating a first CSI reporting procedure associated with the CSI omission technique.

[0013] Figure 3B A signaling diagram illustrating a second CSI reporting procedure associated with the CSI omission technique.

[0014] Figure 4 Is used based on Figure 3A A flowchart of a method for wireless communication at a UE for ML-based / non-ML-based CSI reporting with CSI omission process reporting.

[0015] Figure 5 Is used based on Figure 3A A flow chart of a method for wireless communication at a network entity for ML-based / non-ML-based CSI reporting of a CSI omission process report.

[0016] Figure 6 Is used based on Figure 3B A flowchart of a method for wireless communication at a UE for ML-based / non-ML-based CSI reporting with CSI omission process reporting.

[0017] Figure 7 Is used based on Figure 3B A flow chart of a method for wireless communication at a network entity for ML-based / non-ML-based CSI reporting of a CSI omission process report.

[0018] FIG. 8A to FIG. 8C Table showing ML-based CSI reporting.

[0019] Fig. 9 is a diagram illustrating an example of a hardware implementation of an example UE device.

[0020] Fig.10 is a diagram illustrating an example of a hardware implementation of one or more example network entities. DETAILED DESCRIPTION

[0021] Figure 1A diagram 100 of a wireless communication system associated with a plurality of cells 190 is shown. The wireless communication system includes a user equipment (UE) 102 and a base station / network entity 104. Some base stations may include a converged base station architecture, while other base stations may include a decomposed base station architecture. The converged base station architecture utilizes a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node. The decomposed base station architecture utilizes a protocol stack that is physically or logically distributed between two or more units (e.g., a radio unit (RU) 106, a distributed unit (DU) 108, a central unit (CU) 110). For example, the CU 110 is implemented within a RAN node, and one or more DUs 108 may be co-located with the CU 110, or alternatively, may be geographically or virtually distributed in one or more other RAN nodes. The DU 108 may be implemented to communicate with one or more RUs 106. Any of the RU 106, DU 108, and CU 110 may be implemented as a virtual unit, such as a virtual radio unit (VRU), a virtual distributed unit (VDU), or a virtual central unit (VCU). A base station / network entity 104 (e.g., a converged base station or a disaggregated unit of a base station, such as RU 106 or DU 108) may be referred to as a transmission reception point (TRP).

[0022] The operation and / or network design of the base station 104 can be based on the aggregation characteristics of the base station functions. For example, a decomposed base station architecture is utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN) network, or a virtualized radio access network (vRAN) (which may also be referred to as a cloud radio access network (C-RAN)). Decomposition may include distributing functions between two or more units located at various physical locations, and virtually distributing the functions of at least one unit, which can achieve flexibility in network design. Various units of the decomposed base station architecture or decomposed RAN architecture may be configured to communicate with at least one other unit in wired or wireless communication. For example, the base station 104d / 104e and / or RU 106a-106d may communicate with UE 102a-102d and 102s via one or more radio frequency (RF) access links based on the Uu interface. In an example, multiple RU 106 and / or base station 104 may simultaneously serve UE 102, such as through intra-cell and / or inter-cell access links between UE 102 and RU 106 / base station 104.

[0023] The RU 106, DU 108, and CU 110 may include (or may be coupled to) one or more interfaces configured to transmit or receive information / signals via a wired or wireless transmission medium. For example, a wired interface may be configured to transmit or receive information / signals via a wired transmission medium, such as via a fronthaul link 160 between the RU 106d and a baseband unit (BBU) 112 of a base station 104d associated with the cell 190d. The BBU 112 includes the DU 108 and the CU 110, which may also have a wired interface (e.g., a mid-range link) configured between the DU 108 and the CU 110 to transmit or receive information / signals between the DU 108 and the CU 110. In a further example, a wireless interface, which may include a receiver, transmitter, or transceiver (e.g., an RF transceiver), is configured to transmit and / or receive information / signals via a wireless transmission medium, such as information transmitted between RU 106a of cell 190a and base station 104e of cell 190e via cross-cell communication beams 136-138 of RU 106a and base station 104e.

[0024] The RU 106 may be configured to implement lower layer functions. For example, the RU 106 is controlled by the DU 108 and may correspond to a logical node hosting RF processing functions or lower layer PHY functions, such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc. The functions of the RU 106 may be based on functional partitioning, such as lower layer functional partitioning.

[0025] RU 106 can transmit or receive over-the-air (OTA) communications with one or more UEs 102. For example, RU 106b of cell 190b communicates with UE 102b of cell 190b via a first communication beam set 132 of RU 106b and a second communication beam set 134b of UE 102b, which can correspond to inter-cell communication beams or, in some examples, cross-cell communication beams. For example, UE 102b of cell 190b can communicate with RU 106a of cell 190a via a third communication beam set 134a of UE 102b and a fourth communication beam set 136 of RU 106a. DU 108 can control real-time and non-real-time features of control plane and user plane communications of RU 106.

[0026] Any combination of RU 106, DU 108, and CU 110, or a separate reference thereto, may correspond to base station 104. Thus, base station 104 may include at least one of RU 106, DU 108, or CU 110. Base station 104 provides access to a core network for UE 102. Base station 104 may relay communications between UE 102 and a core network (not shown). Base station 104 may be associated with a macro cell of a high-power cellular base station and / or a small cell of a low-power cellular base station. For example, cell 190e may correspond to a macro cell, and cells 190a-190d may correspond to a small cell. Small cells include femto cells, micro cells, micro cells, and the like. A network including at least one macro cell and at least one small cell may be referred to as a "heterogeneous network."

[0027] Transmissions from the UE 102 to the base station 104 / RU 106 are referred to as uplink (UL) transmissions, while transmissions from the base station 104 / RU 106 to the UE 102 are referred to as downlink (DL) transmissions. Uplink transmissions may also be referred to as reverse link transmissions, while downlink transmissions may also be referred to as forward link transmissions. For example, the RU 106 d utilizes an antenna of the base station 104 d of the cell 190 d to transmit downlink / forward link communications to the UE 102 d, or receive uplink / reverse link communications from the UE 102 d, based on a Uu interface associated with an access link between the UE 102 d and the base station 104 d / RU 106 d.

[0028] The communication link between UE 102 and base station 104 / RU 106 can be based on multiple-input multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming and / or transmit diversity. The communication link can be associated with one or more carriers. UE 102 and base station 104 / RU 106 can utilize Y MHz (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz, 400 MHz, 800 MHz, 1600 MHz, 2000 MHz, etc.) spectrum bandwidth allocated per carrier in up to a total of Yx MHz carrier aggregation, where x component carriers (CCs) are used for communication in each direction of the uplink direction and the downlink direction. The carriers may be adjacent to each other along the spectrum, or may not be adjacent to each other. In an example, uplink carriers and downlink carriers may be allocated in an asymmetric manner, where more or fewer carriers are allocated for the uplink or downlink. The component carriers may include a primary component carrier and one or more secondary component carriers. The primary component carrier may be associated with a primary cell (PCell) and the secondary component carrier may be associated with a secondary cell (SCell).

[0029] Some UEs 102 (such as UEs 102a and 102s) may perform device-to-device (D2D) communication via a sidelink. For example, the sidelink communication / D2D link utilizes the spectrum of a wireless wide area network (WWAN) associated with uplink and downlink communications. Such sidelink / D2D communication may be performed via various wireless communication systems, such as a wireless fidelity (Wi-Fi) system, a Bluetooth system, a long term evolution (LTE) system, a new radio (NR) system, etc.

[0030] The electromagnetic spectrum is typically subdivided into different categories, bands, channels, etc. based on different frequencies / wavelengths associated with the electromagnetic spectrum. Fifth Generation (5G) NR is typically associated with two operating frequency ranges (FRs) referred to as Frequency Range 1 (FR1) and Frequency Range 2 (FR2). FR1 ranges from 410 MHz-7.125 GHz, and FR2 ranges from 24.25 GHz-71.0 GHz, which includes FR2-1 (24.25 GHz-52.6 GHz) and FR2-2 (52.6 GHz-71.0 GHz). FR1 is often referred to as the “sub-6 GHz” band, even though a portion of FR1 is actually greater than 6 GHz. In contrast, FR2 is often referred to as the “millimeter wave” (mmW) band. FR2 is distinct from the “extremely high frequency” (EHF) band, but is an approximate subset of that band, the EHF band ranges from 30 GHz – 300 GHz, and is sometimes also referred to as the “millimeter wave” band. Frequencies between FR1 and FR2 are generally referred to as "mid-band" frequencies. The operating frequency band of mid-band frequencies may be referred to as frequency range 3 (FR3), which ranges from 7.125 GHz to 24.25 GHz. The frequency band within FR3 may include the characteristics of FR1 and / or FR2. Therefore, the characteristics of FR1 and / or FR2 may be extended to mid-band frequencies. Higher operating frequency bands have been identified as extending 5G NR communications to above 52.6 GHz associated with the upper limit of FR2. Three of these higher operating frequency bands include FR2-2 with a range of 52.6 GHz-71.0 GHz, FR4 with a range of 71.0 GHz-114.25 GHz, and FR5 with a range of 114.25 GHz-300 GHz. The upper limit of FR5 corresponds to the upper limit of the EHF band. Therefore, unless otherwise expressly stated herein, the term "below 6 GHz" may refer to frequencies less than 6 GHz, frequencies within FR1, or frequencies that may include mid-band frequencies. Further, unless otherwise expressly stated herein, the term "millimeter wave" or mmW refers to frequencies that may include mid-band frequencies, frequencies that may be within FR2-1, FR4, FR2-2 and / or FR5, or frequencies that may be within the EHF band.

[0031] UE 102 and base station 104 / RU 106 may each include multiple antennas. Multiple antennas may correspond to antenna elements, antenna panels, and / or antenna arrays that may facilitate beamforming operations. For example, RU 106b transmits a downlink beamformed signal to UE 102b based on a first communication beam set 132 in one or more transmit directions of RU 106b. UE 102b may receive a downlink beamformed signal from RU 106b based on a second communication beam set 134b in one or more receive directions of UE 102b. In a further example, UE 102b may also transmit an uplink beamformed signal (e.g., a sounding reference signal (SRS)) to RU 106b based on a second communication beam set 134b in one or more transmit directions of UE 102b. RU 106b may receive an uplink beamformed signal from UE 102b in one or more receive directions of RU 106b.

[0032] UE 102b may perform beam training to determine the best reception and transmission directions for beamformed signals. The transmission and reception directions of UE 102 and base station 104 / RU 106 may be the same, or may be different. In a further example, the beamformed signal may be transmitted between a first base station / RU 106a and a second base station 104e. For example, base station 104e of cell 190e may transmit a beamformed signal to RU 106a based on communication beam 138 in one or more transmission directions of base station 104e. RU 106a may receive a beamformed signal from base station 104e of cell 190e based on RU communication beam 136 in one or more reception directions of RU 106a. In a further example, base station 104e transmits a downlink beamformed signal to UE 102e based on communication beam 138 in one or more transmission directions of base station 104e. The UE 102e receives downlink beamformed signals from the base station 104e in one or more receive directions of the UE 102e based on the UE communication beam 130. The UE 102e may also transmit uplink beamformed signals to the base station 104e in one or more transmit directions of the UE 102e based on the UE communication beam 130, so that the base station 104e may receive uplink beamformed signals from the UE 102e in one or more receive directions of the base station 104e.

[0033] The base station 104 may include and / or be referred to as a network entity. That is, a "network entity" may refer to a base station 104 or at least one unit of a base station 104, such as a RU 106, a DU 108, and / or a CU 110. The base station 104 may also include and / or be referred to as a next generation evolved Node B (ng-eNB), a next generation NB (gNB), an evolved NB (eNB), an access point, a base station transceiver, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP, a network node, a network device, or other related terms. The base station 104 or an entity at the base station 104 may be implemented as an IAB node, a relay node, a side link node, an aggregated (integrated) base station, or a decomposed base station including one or more of the RU 106, the DU 108, and / or the CU 110. A set of aggregated or decomposed base stations may be referred to as a next generation radio access network (NG-RAN). In some examples, UE 102a operates in dual connectivity (DC) with base station 104e and base station / RU 106a. In such a case, base station 104e may be the primary node and base station / RU 160a may be the secondary node.

[0034] The uplink / downlink signaling may also be communicated via a satellite positioning system (SPS) 114. In an example, the SPS 114 of the cell 190c may communicate with one or more UEs 102 (such as UE 102c) and one or more base stations 104 / RUs 106 (such as RU 106c). The SPS 114 may correspond to one or more of a global navigation satellite system (GNSS), a global positioning system (GPS), a non-terrestrial network (NTN), or other satellite positioning / positioning systems. The SPS 114 may be associated with LTE signals, NR signals (e.g., based on round trip time (RTT) and / or multiple RTT), wireless local area network (WLAN) signals, terrestrial beacon systems (TBS), sensor-based information, NR enhanced cell ID (NR E-CID) technology, downlink departure angle (DL-AoD), downlink arrival time difference (DL-TDOA), uplink arrival time difference (UL-TDOA), uplink arrival angle (UL-AoA), and / or other systems, signals, or sensors.

[0035] Still refer to Figure 1In certain aspects, any of the UEs 102 may include a UE-based channel state information (CSI) processing component 140 configured to receive a channel state information-reference signal (CSI-RS) associated with a first CSI report from a network entity; and transmit a CSI report transmission to the network entity, wherein when a total payload size of the CSI report transmission exceeds a threshold, at least a first portion of the first CSI report is omitted from the CSI report transmission.

[0036] In certain aspects, any of the base stations 104 or a network entity of the base station 104 may include a network-based CSI processing component 150 configured to transmit a CSI-RS associated with a first CSI report to a UE; and receive a CSI report transmission from the UE, wherein based on a total payload size of the CSI report transmission exceeding a threshold, at least a first portion of the first CSI report is omitted from the CSI report transmission.

[0037] therefore, Figure 1 A wireless communication system is described that can be implemented in conjunction with aspects of one or more of the other figures described herein. In addition, although the following description may focus on 5G NR, the concepts described herein may be applicable to other similar areas, such as 5G-Advanced and future versions, LTE, LTE-Advanced (LTE-A), and other wireless technologies such as 6G.

[0038] Figure 2Diagram 200 illustrates an example machine learning (ML) based CSI encoder compression at UE 102 and an example ML based CSI decoder decompression at network entity 104. In a MIMO system, network entity 104 may use CSI to select a digital precoder for UE 102. Network entity 104 may configure CSI reporting 285 via RRC signaling (e.g., CSI-reportConfig), wherein UE 102 uses channel measurement resources (CMRs) to measure CSI-RS 240 to estimate 250 a downlink channel. Network entity 104 may also configure (e.g., via CSI-reportConfig) interference measurement resources (IMRs) for UE 102 to measure interference. Based on the CMRs and IMRs, UE 102 may be able to identify CSI, which may include a rank indicator (RI), a precoding matrix indicator (PMI), a channel quality indicator (CQI), and / or a layer indicator (LI). RI and PMI are used to determine the digital precoder (also known as the precoding matrix), CQI indicates the signal to interference plus noise (SINR), which is used to determine the transmitter's selection of the modulation and coding scheme (MCS). LI is used to identify the strongest layer, such as precoder selection for multi-user (MU)-MIMO pairing and phase tracking reference signal (PT-RS) with low rank transmission and precoder selection.

[0039] The UE 102 may indicate a CSI report 285 via a physical uplink control channel (PUCCH) / physical uplink shared channel (PUSCH) in two parts, where CSI Part 1 may include the RI and CQI of a first transport block (TB), and CSI Part 2 may include the PMI, LI, and CQI of a second TB. The payload size of CSI Part 2 may be based on CSI Part 1, and both parts may be transmitted to the network entity 104 using separate channel coding operations.

[0040] The network entity 104 may configure the time domain behavior (e.g., periodic, semi-persistent, or aperiodic reporting) for the CSI report 285 in CSI-reportConfig. The network entity 104 may activate or deactivate semi-persistent CSI reporting via a media access control-control element (MAC-CE). The network entity 104 may also trigger aperiodic CSI reporting via downlink control information (DCI). The UE 102 may report periodic CSI on PUCCH resources configured in CSI-reportConfig. The UE 102 may report semi-persistent CSI on PUCCH resources configured in CSI-reportConfig or on PUSCH resources triggered by DCI from the network entity 104. The UE 102 may report aperiodic CSI on PUSCH resources triggered by DCI from the network entity 104.

[0041] In resource element k of CSI-RS 240, the received signal in the frequency domain can be obtained as follows: in, Indicates the effective channel including the simulated beamforming weights, whose dimension is N Rx × N Tx , Indicates the CSI-RS 240 at resource element k, Indicates interference plus noise, N Rx indicates the number of receiving ports, and N Tx Indicates the number of transport ports.

[0042] In resource element k of the physical downlink shared channel (PDSCH), the received signal in the frequency domain may correspond to: in Indicates the precoder. Typically, the precoder should be the same for subcarriers within a subband (eg, bundled physical resource blocks (PRBs)).

[0043] The UE 102 may measure and report CSI using a Type 2 CSI codebook, where the precoder performs quantization based on: in Corresponding to dimension N Tx × 2L wideband precoder; corresponds to a subband precoder of dimension 2L × v; L corresponds to the number of beams; and v corresponds to the number of layers, which can be RI+1.

[0044] Can be quantized based on the codebook, and The quantization can be done based on the power and angle of each element, which may result in large overhead because is based on subbands, and the CSI report 285 may have multiple subbands, which may be determined based on the bandwidth of the CSI-RS 240. In an example, the codebook for W1 selection may correspond to: in, represents the Kronecker product; L indicates the number of beams configured by RRC signaling; N1, N2, O1, and O2 correspond to the number of ports and oversampling factors in the horizontal and vertical domains that can be configured via RRC signaling. The candidate value may be based on the number of CSI-RS ports. The codebook includes precoders with different values ​​of m and n. In the example, the candidate value is based on a predefined protocol.

[0045] ML is an example technique that the UE 102 may implement to perform CSI compression 270a, where the first v columns of the eigenvector of the average channel for each subband may be used as input. As used herein, the terms "machine learning" and "artificial intelligence" may be used interchangeably with each other unless expressly stated otherwise.

[0046] Diagram 200 shows an example of ML-based CSI compression after UE 102 receives CSI-RS 240 from network entity 104. UE 102 may perform channel estimation 250 based on CSI-RS 240 and calculate 260a an eigenvector of the channel in each subband. The eigenvector may be input to a neural network for CSI encoder compression 270a. UE 102 transmits 280a a compressed CSI report 285 to network entity 104.

[0047] The network entity 104 performs CSI report detection 280b on the CSI report transmission 280a from the UE 102. The neural network at the network entity 104 decodes the compressed CSI report 285 via CSI decoder decompression 270b to recover the eigenvectors. The network entity 104 selects 260b a precoder for each subband based on the reported eigenvectors.

[0048] For each subband, the eigenvector V can be derived based on the singular vector decomposition (SVD) of the average channel in the subband as follows: Where N indicates the number of CSI-RS resource elements of subband S; Indicates the estimated channel based on the CSI-RS 240 at resource element k.

[0049] ML-based CSI compression techniques may refer to the following terms:

[0050] Data collection refers to the process by which a network node, management entity, or UE 102 collects data for ML model training, data analysis, and inference.

[0051] An ML model refers to a data-driven algorithm that applies ML techniques to generate an output set based on an input set.

[0052] ML model training refers to the process of training an ML model in a data-driven manner (e.g., by learning input / output relationships) to obtain the trained ML model for inference.

[0053] ML model inference refers to the process of using a trained ML model to generate an output set based on an input set.

[0054] ML model validation refers to a subprocess of ML model training for evaluating the quality of an ML model using a dataset different from the training dataset used for model training. The different data can be used to select model parameters that generalize data outside the dataset used for ML model training.

[0055] ML model testing refers to a subprocess of ML model training for evaluating the performance of a trained ML model using a dataset different from the training dataset used for ML model training and validation. Unlike ML model validation, testing does not assume subsequent adjustments to the ML model.

[0056] The UE-side ML model refers to an ML model whose inference is performed at the UE 102 .

[0057] The network-side ML model refers to an ML model whose inference is performed at the network / network entity 104 .

[0058] A one-sided ML model refers to a UE-side ML model or a network-side ML model.

[0059] A two-sided ML model refers to a paired ML model on which joint reasoning is performed, where the joint reasoning includes ML reasoning performed jointly across the UE 102 and the network entity 104 (e.g., a first portion of the reasoning is performed by the UE 102 and the remainder of the reasoning is performed by the network entity 104, or vice versa).

[0060] ML model delivery refers to the delivery of ML models over the air interface based on the parameters of the model structure known at the receiving end or a new model with parameters. The delivery technology can include the delivery of a complete ML model or a partial ML model.

[0061] Model download refers to the transfer of the ML model from the network entity 104 to the UE 102 .

[0062] Model upload refers to the transfer of the ML model from the UE 102 to the network entity 104 .

[0063] Federated learning / federated training refers to a machine learning technique that trains ML models across multiple distributed edge nodes (e.g., UEs, network entities, etc.), with each edge node performing local model training using local data samples. Federated learning / training can be based on multiple interactions with the ML model without exchanging local data samples.

[0064] Offline field data refers to data collected from the field and used for offline training of ML models.

[0065] Online field data refers to data collected from the field and used for online training of ML models.

[0066] Model monitoring refers to the process used to monitor the inference performance of ML models.

[0067] Supervised learning refers to the process of training a model based on inputs and corresponding labels.

[0068] Unsupervised learning refers to the process of training a model without the need for labeled data.

[0069] Semi-supervised learning refers to the process of training a model based on a mixture of labeled and unlabeled data.

[0070] Reinforcement learning (RL) is the process of training an ML model based on the inputs (aka states) and feedback signals (aka rewards) generated from the model’s outputs (aka actions) in the environment that the model interacts with.

[0071] Model activation refers to enabling an ML model for a specific feature.

[0072] Model deactivation refers to disabling an ML model for a specific feature.

[0073] Model switching refers to deactivating the currently active ML model and activating a different ML model for a specific function.

[0074] The UE 102 may configure multiple CSI-reportConfigs. In some examples, the configuration may be based on ML techniques (e.g., ML-based CSI reporting). In other examples, the UE 102 may be configured based on codebook techniques (e.g., non-ML-based CSI reporting). The network entity 104 may trigger the UE 102 to report CSI based on multiple CSI-reportConfigs via PUSCH or PUCCH. If the total payload size of the CSI report exceeds the maximum payload size of CSI part 2 in PUSCH / PUCCH, some parts of the CSI report may need to be omitted. For multiple ML-based CSI reports or hybrid ML-based / non-ML-based CSI reports, the UE 102 may implement techniques for CSI omission in multiple ML-based / non-ML-based CSI reports and CSI omission in separate ML-based CSI reports.

[0075] Figure 3AA signaling diagram 300 of a first CSI reporting procedure associated with a CSI omission technique is shown. If the overhead of multiple CSI reports exceeds the maximum payload size of the PUSCH / PUCCH for the CSI reports, then CSI part 2 may be omitted from the CSI report based on a priority rule such that the total payload size of the CSI reports is reduced to a size less than or equal to the maximum payload size supported by the PUSCH / PUCCH. CSI reports with a higher priority "value" (i.e., lower overall priority) may also be omitted from the reporting procedure. For example, a first CSI report with priority value = 1 has a lower priority than a second CSI report with priority value = 0 and may be omitted if the total payload size of the CSI reports exceeds the maximum payload size.

[0076] The network entity 104 transmits 302 RRC signaling to the UE 102 to configure at least one ML-based CSI report (e.g., CSI-reportConfig). When the codebookType in the CSI-reportConfig is set to a first specific value (e.g., 'ai-Ml' or 'type3'), or the reportQuantity in the CSI-reportConfig is set to a second specific value (e.g., 'ri-compressedPmi-cqi'), the CSI report may be ML-based. The network entity 104 may optionally configure 302 at least one non-ML-based CSI report, which may be based on a specific codebook (e.g., Type1 or Type2 codebook). The configuration may further include information that the UE 102 omits part or all of the ML-based CSI report or the non-ML-based CSI report when the total payload size of the CSI report transmission from the UE 102 exceeds a threshold of the maximum payload size.

[0077] The network entity 104 may trigger 304 at least one ML-based CSI report (e.g., via MAC-CE or DCI). In an example, the network entity 104 transmits 304 a MAC-CE to activate semi-persistent CSI reporting. In other examples, the network entity 104 transmits 304 a DCI to trigger aperiodic CSI reporting. The UE 102 may report periodic CSI reporting via uplink resources configured 302 via RRC signaling from the network entity 104.

[0078] The UE 102 receives 306 a CSI-RS associated with the triggered or configured CSI report from the network entity 104. The UE 102 may perform 308a a CSI measurement of the CSI-RS associated with the triggered or configured CSI report to determine the CSI for the CSI report. If the network entity 104 configures 302 or triggers 304 multiple CSI reports with a total payload size exceeding the maximum payload size of the PUCCH / PUSCH for the CSI report, the UE 102 may determine to implement a CSI omission procedure to omit 308 the CSI report from the CSI report transmission 310 to the network entity 104 (e.g., Figure 3B ) or the CSI report section (as Figure 3A That is, the UE 102 may transmit 310 a CSI report transmission to the network entity 104, wherein at least a first portion of the CSI report is omitted. In some implementations, for example Figure 3A As shown, omitting at least a first portion of the CSI report may refer to omitting a low priority portion of the CSI report from the ML-based / non-ML-based CSI report in the CSI report transmission. In the signaling diagram 300, the UE 102 determines the priority of each portion of the ML-based / non-ML-based CSI report. If the CSI omission technique is to be implemented for the ML-based / non-ML-based CSI report, the UE 102 may omit 308a the low priority portion of the ML-based / non-ML-based CSI report from the CSI transmission. In other implementations, for example Figure 3B As shown, omitting at least the first part of the CSI report may refer to replacing the ML-based CSI report with a non-ML-based CSI report in the CSI report transmission.

[0079] In order to transmit 310 a CSI report transmission that omits at least a first portion of a CSI report, the UE 102 transmits 310a a CSI report in diagram 300 that has the remaining (i.e., non-omitted) content of an ML-based / non-ML-based CSI report that has been triggered 304 or configured 302 by the network entity 104. That is, the UE 102 transmits 310a a CSI report that omits a low priority portion from the ML-based / non-ML-based CSI report. The CSI report transmission may include an indication of the omitted portion of the ML-based / non-ML-based CSI report. The CSI report may be transmitted 310a on a PUCCH or a PUSCH. For a PUCCH transmission, the CSI report may be reported using a long PUCCH format. For a PUSCH transmission, the CSI report may be reported using a short PUCCH format.

[0080] The network entity 104 identifies 312a the portion omitted from the CSI report based on the CSI omission procedure / technique and decodes 312a the remaining portion of the ML-based / non-ML-based CSI report received 310a from the UE 102 . Figure 3A A CSI omission technique is shown for omitting 308a a portion of an ML-based / non-ML-based CSI report, while Figure 3B A CSI omission technique is shown that includes replacing a more complex ML-based CSI report with a less complex non-ML-based CSI report in a CSI report transmission.

[0081] Figure 3B A signaling diagram 350 is shown for a second CSI reporting process associated with a CSI omission technique. Elements 302, 304, and 306 have been described with respect to Figure 3A Described.

[0082] The UE 102 performs 308b CSI measurement on one or more CSI-RS received 306 from the UE 102. The UE 102 may also prioritize the ML-based CSI report and the non-ML-based CSI report. In the signaling diagram 350, if a portion of the ML-based CSI report is to be omitted from the CSI report transmission, the UE 102 may replace 308b the entire ML-based CSI report with the non-ML-based CSI report in the CSI report transmission. The UE 102 may also combine the ML-based / non-ML-based CSI reports included in the CSI report transmission by omitting portions of other ML-based / non-ML-based CSI reports and / or by omitting portions of the substituted non-ML-based CSI report. Figure 3A and Figure 3B The CSI omission technique is used to further reduce the payload size of the CSI report transmission.

[0083] In order to transmit 310 a CSI report transmission in which at least a first portion of a CSI report is omitted, the UE 102 transmits 310b a CSI report having at least one non-ML based CSI substitute report for an ML based CSI report to the network entity 104 in diagram 350. The UE 102 may also transmit 310b other ML based / non-ML based CSI reports that omit a lower priority portion from the CSI report transmission, and / or transmit 310b a non-ML based CSI substitute that omits a lower priority portion of a non-ML based CSI substitute report from the CSI report transmission. The CSI report transmission may include an indication of an omitted / substituted ML based CSI report and an indication of a non-ML based CSI report that serves as a substitute for the omitted / substituted ML based CSI report. The non-ML based CSI substitute report may be transmitted 310b on a PUCCH or a PUSCH. For a PUCCH transmission, the CSI report may be reported using a long PUCCH format. For a PUSCH transmission, the CSI report may be reported using a short PUCCH format.

[0084] The network entity 104 identifies 312b the replacement / omission of the CSI report and decodes 312b the CSI report received 310b. Figure 3B The process in may provide greater flexibility for the UE 102 to transmit 310 the CSI report in an efficient manner, but Figure 3B The process in may also have higher UE complexity because in addition to the ML-based techniques for CSI reporting, the UE 102 may also measure CSI-RS according to non-ML-based techniques. Figures 4 to 7 Shown is a method for implementing FIG. 3A to FIG. 3B Specifically, Figure 4 and Figure 6 UE 102 is shown FIG. 3A to FIG. 3B Implementation of one or more aspects. Figure 5 and Figure 7 The network entity 104 is shown FIG. 3A to FIG. 3B Implementation of one or more aspects.

[0085] Figure 4 Shown for reporting and Figure 3A 400 of a method for wireless communication at a UE for ML-based / non-ML-based CSI reporting associated with a CSI omission process. Figure 3A and Fig. 9, the method can be performed by UE 102, UE device 902, etc., which may include memory 926', 906', 916 and may correspond to the entire UE 102 or the entire UE device 902, or components of UE 102 or UE device 902, such as a wireless baseband processor 926 and / or an application processor 906.

[0086] UE 102 receives 402 from a network entity a configuration for omitting at least a first portion of a first CSI report from a CSI report transmission when a total payload size of the CSI report transmission exceeds a threshold. FIG. 3A to FIG. 3B , the UE 102 receives 302 a configuration for ML-based CSI reporting and optionally for non-ML-based CSI reporting from the network entity 104 , wherein the UE 102 may omit 308 part or all of the CSI report from transmission to the network entity 104 .

[0087] UE 102 receives 404 a trigger indication for a first CSI report from a network entity, wherein at least a first portion of the first CSI report is omitted from the CSI report transmission when the total payload size of the CSI report transmission exceeds a threshold. FIG. 3A to FIG. 3B , the UE 102 receives 304 a trigger indication for ML-based CSI reporting from the network entity 104 .

[0088] UE 102 receives 406 a CSI-RS associated with a first CSI report from a network entity. FIG. 3A to FIG. 3B , the UE 102 receives 306 one or more CSI-RS associated with the configured / triggered CSI report from the network entity 104 to perform 308 CSI measurement on the one or more CSI-RS.

[0089] Based on the total payload size of the CSI transmission exceeding the threshold and based on the first part of the CSI report having a lower priority than the second part of the CSI report, the UE 102 omits 408a the first part of the first CSI report from the CSI report transmission. Figure 3A , the UE 102 omits 308a a portion having a low priority in the ML based / non-ML based CSI report according to the priority of each portion of the ML based / non-ML based CSI report.

[0090] To transmit a CSI report transmission that omits at least the first portion of the CSI report, the UE 102 transmits 410a a CSI report transmission to a network entity, the CSI report transmission including the second portion of the CSI report and omitting the first portion of the first CSI report from the CSI report transmission. Figure 3A, the UE 102 transmits 310a to the network entity 104 a CSI report omitting the low priority portion from the ML-based / non-ML-based CSI report. Figure 4 A method from the UE side of a wireless communication link is described, while Figure 5 A method from the network side of a wireless communication link is described.

[0091] Figure 5 is used with Figure 3A Flowchart 500 of a method of wireless communication at a network entity for ML-based / non-ML-based CSI reporting associated with a CSI omission process. Figure 3A and Fig.10 , the method may be performed by one or more network entities 104, which may correspond to a base station or a unit of a base station, such as RU 106, DU 108, CU 110, RU processor 1006, DU processor 1026, CU processor 1046, etc. One or more network entities 104 may include a memory 1006' / 1026' / 1046', which may correspond to the entirety of one or more network entities 104, or a component of one or more network entities 104, such as RU processor 1006, DU processor 1026, or CU processor 1046.

[0092] The network entity 104 transmits 502 to the UE a configuration for omitting at least a first portion of a first CSI report from a CSI report transmission when the total payload size of the CSI report transmission exceeds a threshold. FIG. 3A to FIG. 3B , the network entity 104 transmits 302 to the UE 102 a configuration for ML-based CSI reporting and optionally for non-ML-based CSI reporting to omit part or all of the CSI report from CSI report reception from the UE 102 .

[0093] The network entity 104 transmits 504 to the UE a trigger indication for the first CSI report, wherein at least a first portion of the first CSI report is omitted from the CSI report transmission when the total payload size of the CSI report transmission exceeds a threshold. FIG. 3A to FIG. 3B , the network entity 104 transmits 304 to the UE 102 a trigger indication for ML-based CSI reporting.

[0094] The network entity 104 transmits 506 the CSI-RS associated with the first CSI report to the UE. FIG. 3A to FIG. 3B , the network entity 104 transmits 306 one or more CSI-RS associated with the configured / triggered CSI report to the UE 102 to receive CSI feedback from the UE 102.

[0095] To receive a CSI report transmission that omits at least a first portion of a CSI report, the network entity 104 receives 510a a CSI report transmission from the UE, the CSI report transmission including a second portion of a first CSI report, but omits the first portion of the first CSI report from the CSI report transmission based on a total payload size of the CSI report transmission exceeding a threshold and based on the first portion of the CSI report having a lower priority than the second portion of the CSI report. For example, referring to Figure 3A , the network entity 104 receives 310a from the UE 102 a CSI report omitting a low priority portion from the ML-based / non-ML-based CSI report.

[0096] The network entity 104 decodes 512a the second portion of the first CSI report based on the indication to omit the first portion of the CSI report from the CSI report transmission. Figure 3A , the network entity 104 identifies 312a the omitted portion from the CSI report and decodes 312a the remaining portion of the ML-based / non-ML-based CSI report.

[0097] Figure 6 Shown for reporting and Figure 3B Flowchart 600 of a method of wireless communication at a UE for ML-based / non-ML-based CSI reporting associated with a CSI omission process. Figure 3B and Fig. 9 , the method can be performed by UE 102, UE device 902, etc., which may include memory 926', 906', 916 and may correspond to the entire UE 102 or the entire UE device 902, or components of UE 102 or UE device 902, such as a wireless baseband processor 926 and / or an application processor 906.

[0098] Elements 402, 404 and 406 have been described with respect to Figure 4 Described.

[0099] UE 102 omits 608b the first (e.g., ML-based) CSI report from the CSI report transmission based on the total payload size of the CSI transmission exceeding the threshold—the first (e.g., ML-based) CSI report is replaced with a second (e.g., non-ML-based) CSI report. Figure 3B , the UE 102 performs 308b CSI measurement on the CSI-RS, and if a portion of the ML-based CSI report is to be omitted from the CSI report transmission, the UE 102 replaces 308b the ML-based CSI report in the CSI report transmission with a non-ML-based CSI report (e.g., according to the priority of the ML-based CSI report).

[0100] To transmit a CSI report transmission that omits at least a first portion of the CSI report, the UE 102 transmits 610b a CSI report transmission to a network entity, the CSI report transmission including a second (e.g., non-ML-based) CSI report that replaces the omitted first (e.g., ML-based) CSI report. Figure 3B , the UE 102 transmits 310b to the network entity 104 a CSI report having at least one non-ML based CSI substitute report for the ML based CSI reporting. Figure 6 A method from the UE side of a wireless communication link is described, while Figure 7 A method from the network side of a wireless communication link is described.

[0101] Figure 7 is used with Figure 3B Flowchart 700 of a method of wireless communication at a network entity for ML-based / non-ML-based CSI reporting associated with a CSI omission process. Figure 3B and Fig.10 , the method may be performed by one or more network entities 104, which may correspond to a base station or a unit of a base station, such as RU 106, DU 108, CU 110, RU processor 1006, DU processor 1026, CU processor 1046, etc. One or more network entities 104 may include a memory 1006' / 1026' / 1046', which may correspond to the entirety of one or more network entities 104, or a component of one or more network entities 104, such as RU processor 1006, DU processor 1026, or CU processor 1046.

[0102] Elements 502, 504 and 506 have been described with respect to Figure 5 Described.

[0103] To receive a CSI report transmission that omits at least a first portion of a CSI report, the network entity 104 receives 710b a CSI report transmission from the UE, the CSI report transmission including a second (e.g., non-ML-based) CSI report that replaces the first (e.g., ML-based) CSI report in the CSI report transmission based on a total payload size of the CSI report transmission exceeding a threshold. For example, referring to Figure 3B , the network entity 104 receives 310b from the UE 102 a CSI report having at least one non-ML based CSI substitute report for the ML based CSI report.

[0104] The network entity 104 decodes 712b the second (e.g., non-ML-based) CSI report based on an indication that the first (e.g., ML-based) CSI report is omitted from the CSI report transmission and is replaced by the second (e.g., non-ML-based) CSI report. Figure 3B , the network entity 104 identifies 312b the replacement / omission of the CSI report and decodes 312b the received ML-based / non-ML-based CSI report.

[0105] The RRC signaling may indicate an RRC reconfiguration message from the network entity 104 to the UE 102, or a system information block (SIB), where the SIB may be an existing SIB (e.g., SIB1) or a new SIB transmitted by the network entity 104. In addition, the network entity 104 may obtain the UE capability via UE capability report signaling or from the core network (e.g., access and mobility management function (AMF)). Whether ML-based CSI reporting (e.g., CSI measured using a CSI encoder based on an ML model) and non-ML-based CSI reporting (e.g., CSI measured based on a Type 1 / Type 2 codebook) can be multiplexed and reported on the PUSCH / PUCCH may be indicated by the UE capability, or may be predefined or configured by higher layer signaling (e.g., RRC signaling) from the network entity 104.

[0106] The priorities of ML-based CSI reports and non-ML-based CSI reports may be different. The network entity 104 and the UE 102 may determine the priority of the CSI report based on the time domain behavior of the CSI report (e.g., periodic / semi-persistent / aperiodic reporting), CSI-reportConfigId, serving cell ID, number of reports, and whether the CSI-RS is measured based on ML or non-ML technology.

[0107] In the example, the priority can be based on , where if the priority value is lower, the actual priority is higher (e.g., priority 0 corresponds to a higher priority than priority 1). A value of k=0 may indicate a CSI report including L1-RSRP or L1-SINR information, a value of k=1 may indicate a non-ML-based CSI report not including L1-RSRP or L1-SINR information, and a value of k=2 may indicate an ML-based CSI report not including L1-RSRP or L1-SINR information. In addition, Indicates aperiodic CSI reporting transmitted on PUSCH, Indicates a semi-persistent CSI report transmitted on PUSCH, indicates a semi-persistent CSI report transmitted on the PUCCH, and indicates a periodic CSI report transmitted on the PUCCH. Also, c corresponds to the serving cell index, and corresponds to the value of the higher layer parameter maxNrofServingCells. In addition, s corresponds to reportConfigID, and Corresponding to the value of the higher layer parameter maxNrofCSI-Report Configurations. Alternatively, k=0 may indicate a CSI report carrying L1-RSRP or L1-SINR, and k=1 may indicate an ML-based CSI report not carrying L1-RSRP or L1-SINR, and k=2 may indicate a non-ML-based CSI report not carrying L1-RSRP or L1-SINR. In another implementation, the network entity 104 and the UE 102 may assume that an ML-based CSI report has a higher or lower priority than a non-ML-based CSI report.

[0108] FIG. 8A to FIG. 8C Tables 800-820 of ML-based CSI reporting are shown. The CSI omission process can be associated with a technique in which the ML-based CSI report includes: (1) a first PMI of the ML-based CSI report indicating compressed feature vectors of channels in all subbands; (2) a first PMI indicating a selected wideband precoder and a second PMI indicating compressed feature vectors of precoded channels with the selected wideband precoder in all subbands; or (3) a first PMI indicating a selected wideband precoder, a second PMI indicating compressed feature vectors of precoded channels with the selected wideband precoder in even subbands, and a third PMI indicating compressed feature vectors of precoded channels with the selected wideband precoder in odd subbands.

[0109] In ML-based CSI reporting, it may be assumed that the wideband PMI and subband PMI have different priorities, which may be applicable to options (2) and (3) described above. The priority value of the wideband PMI is lower than that of the subband PMI. That is, when the omission is implemented, the subband PMI may be omitted. In options (2) and (3), the priorities of the first PMI and the second / third PMI may be different. The network entity 104 and the UE 102 may select the subband PMI according to the priority value of the wideband PMI. Fig. 8A Table 800 determines N Rep ML-based priorities for CSI reports, where CSI report n corresponds to the CSI report with the nth smallest Pri i,CSI CSI report of (y,k,c,s) values.

[0110] In another embodiment, the network entity 104 and the UE 102 assume that a single priority value is assigned to the ML-based CSI report. Such a technique may be applicable to all of the above options (1), (2), and (3). Figure 8B Table 810 is based on Pri i,CSI (y, k, c, s) to determine the CSI omission priority.

[0111] In another example, the network entity 104 and the UE 102 assume that ML-based CSI reporting is divided into 3 priority groups. The priority groups may correspond to: Group 0 for wideband PMI (e.g., first PMI), Group 1 for subband PMI for even subbands (e.g., second PMI), and Group 2 for subband PMI for odd subbands (e.g., third PMI). For ML-based CSI reporting, ML-based CSI compression may be performed twice, where a first instance of compression is for even subbands and a second instance of compression is for odd subbands.

[0112] In another example, the priority groups may correspond to: group 0 for wideband PMI (e.g., the first PMI), group 1 for high priority bits for subband PMI (e.g., a portion of the second PMI), and group 2 for low priority bits for subband PMI (e.g., the other portion of the second PMI). With the high priority bits, the network entity 104 may be able to recover the first feature vector with some loss. The high priority of the bits may depend on the ML model and may be predefined or configured by higher layer signaling (e.g., RRC signaling in CSI-reportConfig) from the network entity 104. The bits in group 1 and group 2 may be generated based on a single ML model or separate ML models.

[0113] In another implementation, the priority groups may correspond to: group 0 for high priority bits of the PMI in CSI part 2, group 1 for medium priority bits of the PMI in CSI part 2, and group 2 for low priority bits of the PMI in CSI part 2. Based on the bits having different priorities, the network entity 104 may be able to recover the first feature vector with a certain loss. The bits having high / medium / low priority may depend on the neural network architecture and may be predefined or configured by higher layer signaling from the network entity 104 (e.g., RRC signaling in CSI-reportConfig). The bits in groups 1 / 2 / 3 may be generated based on a single ML model or separate ML models. Thus, the bits may be generated based on the ML model. Figure 8C Table 820 defines the CSI omission priority.

[0114] In an example, if a portion of the ML-based CSI report is to be omitted, the UE 102 may fall back to reporting a non-ML-based CSI report based on a fallback codebook (e.g., a Type2 CSI codebook or an eType2 CSI codebook). The fallback codebook may be configured via RRC signaling (e.g., an RRC parameter in CSI-reportConfig), or predefined or reported via UE capability signaling. The UE 102 may perform CSI omission based on a predefined protocol associated with non-ML-based CSI reporting.

[0115] In some examples, the network entity 104 may implement the same CSI omission process to determine whether the UE reports an ML-based CSI report or a non-ML-based CSI report in place of the ML-based CSI report. In other implementations, for CSI reporting, the UE 102 may report an additional indicator to report whether the CSI is measured based on an ML or non-ML technique. In an example, such an indicator (e.g., an indicator for reporting a codebook type used for CSI reporting) may be explicitly transmitted in the CSI report. In other examples, the network entity 104 may configure or trigger two PUCCH / PUSCH resources for CSI reporting, wherein a first resource is used for ML-based CSI reporting and a second resource is used for non-ML-based CSI reporting. By selecting the corresponding PUCCH / PUSCH resource, the UE 102 may implicitly report whether the CSI is based on an ML technique or a non-ML technique. In another example, such an indicator may be implicitly reported based on a scrambling sequence of a demodulation reference signal (DMRS) or PUCCH / PUSCH, wherein a first scrambling sequence is used to indicate an ML-based CSI report and a second scrambling sequence is used to indicate a non-ML-based CSI report. By receiving such an indicator, the network entity 104 may determine whether to decode the CSI as an ML-based CSI report or a non-ML-based CSI report.

[0116] The UE 102 may use multiple ML models with different compression rates for CSI compression, where the ML models may be configured or predefined via RRC signaling. Similarly, the network entity 104 may use multiple ML models with different compression rates for CSI decompression. The UE 102 may select the ML model with the highest compression rate that meets the overhead constraints of the PUCCH / PUSCH for CSI reporting. The network entity 104 may apply the same CSI omission process to determine the ML model for CSI decompression, where the ML model with the highest compression rate that meets the overhead constraints of the PUCCH / PUSCH for CSI reporting is selected.

[0117] UE 102 may report an additional indicator to report the ML model index. The network entity 104 and UE 102 maintain a list of ML models with different compression rates. Such an ML model may be configured by RRC signaling from the network entity 104 to the UE 102, or by a UE capability report, or may be predefined. The indicator may be explicitly transmitted in the CSI report (e.g., an indicator for explicitly reporting the ML model index). The network entity 104 may configure or trigger X PUCCH / PUSCH resources for the CSI report, where each resource is used for one ML model. By selecting the corresponding PUCCH / PUSCH resource, the UE 102 may implicitly report the ML model index. In another example, the indicator may be implicitly reported based on a scrambling sequence of a DMRS or PUCCH / PUSCH, where each scrambling sequence corresponds to an ML model. By receiving such an indicator, the network entity 104 may determine the ML model to be used for CSI decompression.

[0118] For N Rep,1 ML-based CSI reports and N Rep,2 For each non-ML-based CSI report, the network entity 104 and the UE 102 may assume that the ML-based CSI report has a higher or lower priority value than the non-ML-based CSI report. The UE 102 may determine to omit the ML-based or non-ML-based CSI report in different implementations, and if further omissions are to be implemented, the UE 102 may apply CSI omission to the remaining non-ML-based or ML-based CSI reports. The above example may be implemented for ML-based CSI omission, and the predefined protocol may be implemented for non-ML-based CSI omission. For N Rep,1 ML-based CSI reports and N Rep,2 For non-ML based CSI reports, the network entity 104 and the UE 102 may determine the priority of the CSI report based on the priority of the CSI-reportConfig. The priority within the CSI-reportConfig may be determined based on the priority group.

[0119] Fig. 9900 is a diagram illustrating an example of a hardware implementation of a UE device 902. The UE device 902 may be a UE 102, a component of a UE 102, or may implement UE functionality. The UE device 902 may include an application processor 906, which may have an on-chip memory 906'. In an example, the application processor 906 may be coupled to a secure digital (SD) card 908 and / or a display 910. The application processor 906 may also be coupled to a sensor module 912, a power supply 914, an additional memory module 916, a camera 918, and / or other related components. For example, the sensor module 912 may control a barometric pressure sensor / altimeter, a motion sensor (such as an inertial management unit (IMU)), a gyroscope, an accelerometer, a light detection and ranging (LIDAR) device, a radio-aided detection and ranging (RADAR) device, a sound navigation and ranging (SONAR) device, a magnetometer, an audio device, and / or other technologies for positioning.

[0120] The UE device 902 may further include a wireless baseband processor 926, which may be referred to as a modem. The wireless baseband processor 926 may have an on-chip memory 926'. Together with and similar to the application processor 906, the wireless baseband processor 926 may also be coupled to a sensor module 912, a power supply 914, an additional memory module 916, a camera 918, and / or other related components. The wireless baseband processor 926 may additionally be coupled to one or more subscriber identity modules (SIM) cards 920 and / or one or more transceivers 930 (e.g., wireless RF transceivers).

[0121] In one or more transceivers 930, the UE device 902 may include a Bluetooth module 932, a WLAN module 934, an SPS module 936 (e.g., a GNSS module), and / or a cellular module 938. The Bluetooth module 932, the WLAN module 934, the SPS module 936, and the cellular module 938 may each include an on-chip transceiver (TRX), or in some cases, include only a transmitter (TX) or only a receiver (RX). The Bluetooth module 932, the WLAN module 934, the SPS module 936, and the cellular module 938 may each include a dedicated antenna and / or utilize an antenna 940 to communicate with one or more other nodes. For example, the UE device 902 may communicate with another UE (e.g., side link communication) and / or communicate with a network entity 104 (e.g., uplink / downlink communication) via an antenna 940 through the transceiver 930, wherein the network entity 104 may correspond to a base station or a unit of a base station, such as a RU 106, a DU 108, or a CU 110.

[0122] The wireless baseband processor 926 and the application processor 906 may each include a computer-readable medium / memory 926', 906', respectively. The additional memory module 916 may also be considered a computer-readable medium / memory. Each computer-readable medium / memory 926', 906', 916 may be non-temporary. The wireless baseband processor 926 and the application processor 906 may each be responsible for general processing, including executing software stored on the computer-readable medium / memory 926', 906', 916. The software, when executed by the wireless baseband processor 926 / application processor 906, enables the wireless baseband processor 926 / application processor 906 to perform various functions described herein. The computer-readable medium / memory may also be used to store data manipulated by the wireless baseband processor 926 / application processor 906 when executing the software. The wireless baseband processor 926 / application processor 906 may be a component of the UE 102. The UE device 902 may be a processor chip (e.g., a modem and / or application) and includes only the wireless baseband processor 926 and / or the application processor 906. In other examples, UE device 902 may be the entire UE 102 and include additional modules of device 902.

[0123] like Figure 1 Discussion and about Figure 4 and Figure 6 In an implementation, a UE-based CSI processing component 140 is configured to receive a CSI-RS associated with a first CSI report from a network entity; and transmit a CSI report transmission to the network entity, wherein when a total payload size of the CSI report transmission exceeds a threshold, at least a first portion of the first CSI report is omitted from the CSI report transmission. The UE-based CSI processing component 140 may be within the application processor 906 (e.g., at 140a), within the wireless baseband processor 926 (e.g., at 140b), or within both the application processor 906 and the wireless baseband processor 926. The UE-based CSI processing components 140a-140b may be one or more hardware components explicitly configured to perform the process / algorithm, implemented by one or more processors configured to perform the process / algorithm, stored in a computer-readable medium for implementation by one or more processors, or some combination thereof.

[0124] Fig.101000 is a diagram illustrating an example of a hardware implementation of one or more network entities 104. One or more network entities 104 may be a base station, a component of a base station, or may implement base station functionality. One or more network entities 104 may include or may correspond to at least one of a RU 106, a DU 108, or a CU 110. The CU 110 may include a CU processor 1046, which may have an on-chip memory 1046'. In some aspects, the CU 110 may further include an additional memory module 1056 and / or a communication interface 1048, both of which may be coupled to the CU processor 1046. The CU 110 may communicate with the DU 108 via a mid-range link 162, such as an F1 interface between the communication interface 1048 of the CU 110 and the communication interface 1028 of the DU 108.

[0125] The DU 108 may include a DU processor 1026, which may have an on-chip memory 1026'. In some aspects, the DU 108 may further include an additional memory module 1036 and / or a communication interface 1028, both of which may be coupled to the DU processor 1026. The DU 108 may communicate with the RU 106 via a fronthaul link 160 between the communication interface 1028 of the DU 108 and the communication interface 1008 of the RU 106.

[0126] The RU 106 may include a RU processor 1006, which may have an on-chip memory 1006'. In some aspects, the RU 106 may further include an additional memory module 1016, a communication interface 1008, and one or more transceivers 1030, all of which may be coupled to the RU processor 1006. The RU 106 may further include an antenna 1040, which may be coupled to the one or more transceivers 1030, such that the RU 106 may communicate with the UE 102 via the antenna 1040 through the one or more transceivers 1030.

[0127] On-chip memory 1006 ', 1026 ', 1046 'and additional memory modules 1016, 1036, 1056 can each be considered as a computer-readable medium / memory. Each computer-readable medium / memory can be non-temporary. Each of the processors 1006, 1026, 1046 is responsible for general processing, including executing software stored on the computer-readable medium / memory. The software, when executed by the corresponding processor 1006, 1026, 1046, enables the processor 1006, 1026, 1046 to perform various functions described herein. The computer-readable medium / memory can also be used to store data manipulated by the processor 1006, 1026, 1046 when executing the software. In an example, the network-based CSI processing component 150 may be located at any of the one or more network entities 104, such as at the CU 110; at both the CU 110 and the DU 108; at each of the CU 110, DU 108, and RU 106; at both the DU 108 and the RU 106; or at the RU 106.

[0128] like Figure 1 Discussion and about Figure 5 and Figure 7 In one embodiment, the network-based CSI processing component 150 is configured to transmit a CSI-RS associated with a first CSI report to a UE; and receive a CSI report transmission from the UE, wherein at least a first portion of the first CSI report is omitted from the CSI report transmission based on a total payload size of the CSI report transmission exceeding a threshold. The network-based CSI processing component 150 may be located within one or more processors of one or more network entities 104, such as a RU processor 1006 (e.g., at 150a), a DU processor 1026 (e.g., at 150b), and / or a CU processor 1046 (e.g., at 150c). The network-based CSI processing components 150a-150c may be one or more hardware components explicitly configured to perform the process / algorithm, implemented by one or more processors 1006, 1026, 1046 configured to perform the process / algorithm, stored in a computer-readable medium for implementation by one or more processors 1006, 1026, 1046, or a combination thereof.

[0129] The specific order or hierarchy of the boxes in the process and flow chart disclosed herein is an illustration of an example method. Therefore, the specific order or hierarchy of the boxes in the process and flow chart can be rearranged. Some boxes can also be merged or deleted. The attached method claims present the elements of each box in the example order, and are not limited to the specific order or hierarchy presented in the claims, process and flow chart.

[0130] The detailed description set forth herein describes various configurations in conjunction with the accompanying drawings, but does not represent the only configuration in which the concepts described herein can be practiced. The detailed description includes specific details for providing a comprehensive explanation of the various concepts. However, these concepts can be practiced without using these specific details. In some cases, well-known structures and components are shown in block diagram form in order to avoid blurring such concepts.

[0131] Various aspects of wireless communication systems (such as telecommunication systems) are presented with reference to various apparatus and methods. These apparatus and methods are described in the detailed description that follows and are illustrated in the accompanying drawings by various blocks, components, circuits, processes, call flows, systems, algorithms, etc. (collectively, "elements"). These elements may be implemented using electronic hardware, computer software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and design constraints imposed on the overall system.

[0132] Elements, or any part of elements or any combination of elements can be implemented as a "processing system" including one or more processors. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on chip (SoCs), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gating logic, discrete hardware circuits, and other similar hardware configured to perform various functions described throughout this disclosure. One or more processors in a processing system can execute software, which can be referred to as software, firmware, middleware, microcode, hardware description language, or other. Software should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software components, applications, software applications, software packages, routines, subroutines, objects, executables, execution threads, processes, functions, or any combination thereof.

[0133] If the functions described herein are implemented in software, the functions may be stored on a computer-readable medium (such as a non-transitory computer-readable storage medium) or encoded as one or more instructions or codes on the computer-readable medium. Computer-readable media include computer storage media and may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), optical disk storage devices, magnetic disk storage devices, other magnetic storage devices, combinations of these types of computer-readable media, or any other medium that can be used to store computer-executable code in the form of computer-accessible instructions or data structures. The storage medium can be any available medium that is accessible to a computer.

[0134] The aspects, implementations, and / or use cases described herein may be implemented across many different platform types, devices, systems, shapes, sizes, and packaging arrangements. For example, the aspects, implementations, and / or use cases may be generated via integrated chip implementations and other non-module component-based devices such as end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / procurement devices, medical devices, artificial intelligence (AI)-enabled devices, machine learning (ML)-enabled devices, and the like. The aspects, implementations, and / or use cases may range from chip-level or modular components to non-modular or non-chip-level implementations, and further to aggregated, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more of the techniques described herein.

[0135] Devices incorporating the various aspects and features described herein may also include additional components and features for implementing and practicing the various aspects and features claimed and described. For example, the transmission and reception of wireless signals necessarily include many components for analog and digital purposes, such as hardware components, antennas, RF chains, power amplifiers, modulators, buffers, processors, interleavers, adders / summers, etc. The techniques described herein may be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc., in various configurations.

[0136] The description herein is provided to enable those skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not limited to the various aspects described herein, but should be interpreted in view of the full scope of the disclosure consistent with the language of the claims.

[0137] Unless explicitly stated, references to singular elements do not mean "one and only one", but rather "one or more". Terms such as "if", "when ..." and "at ..." do not imply an immediate temporal relationship or reaction. That is, these phrases (such as "when ...") do not imply an immediate action in response to the occurrence of an action or during the occurrence of an action, but simply mean that if a certain condition is met, a certain action will occur, but no specific or immediate time constraints are required for the occurrence of the action. Unless explicitly stated otherwise, the term "some" refers to one or more. Combinations such as "at least one of A, B, or C" or "one or more of A, B, or C" include any combination of A, B, and / or C, such as A and B, A and C, B and C, or A and B and C, and may include multiple A, multiple B, and / or multiple C, or may include only A, only B, or only C. A set should be interpreted as a set of elements in which the number of elements is one or more.

[0138] The structural equivalents and functional equivalents of the elements of various aspects described in the entire disclosure known or later learned by those of ordinary skill in the art are expressly incorporated herein by reference and are covered by the claims. The words "module", "mechanism", "element", "device", etc. may not be substitutes for the word "component". Therefore, unless the claim element is clearly stated using the phrase "component for...", any claim element shall not be interpreted as a means plus function. As used herein, the phrase "based on" should not be interpreted as a reference to a closed information set, one or more conditions, one or more factors, etc. In other words, unless clearly stated differently, the phrase "based on A" (where "A" can be information, conditions, factors, etc.) should be interpreted as "at least based on A".

[0139] The following examples are merely illustrative and may be combined with other examples or teachings described herein without limitation.

[0140] Example 1 is a method for performing wireless communications at a UE, comprising: receiving a CSI-RS associated with a first CSI report from a network entity; and transmitting a CSI report transmission to the network entity, wherein when a total payload size of the CSI report transmission exceeds a threshold, at least a first portion of the first CSI report is omitted from the CSI report transmission.

[0141] Example 2 may be combined with Example 1 and include that the CSI report transmission includes multiple CSI reports, the multiple CSI reports including at least one of: an ML-based CSI report or a non-ML-based CSI report.

[0142] Example 3 can be combined with any one of Examples 1 to 2, and includes omitting the at least first part of the first CSI report from the CSI report transmission based on the at least first part of the first CSI report having a lower priority than the second part of the first CSI report.

[0143] Example 4 may be combined with Example 3 and include that the first CSI report is the ML-based CSI report.

[0144] Example 5 may be combined with Example 3 and include that the first CSI report is the non-ML based CSI report.

[0145] Example 6 can be combined with any one of Examples 1 to 2, and includes that the first CSI report is the ML-based CSI report, and includes omitting at least the first part of the first CSI report from the CSI report transmission and further includes: replacing the first CSI report in the CSI report transmission with a second CSI report, and the second CSI report is the non-ML-based CSI report.

[0146] Example 7 may be combined with Example 6 and include replacing the first CSI report with the second CSI report being based on the first CSI report having a priority level lower than a threshold priority level.

[0147] Example 8 can be combined with any one of Examples 6 to 7, and includes the CSI report transmission having the multiple CSI reports and including the second CSI report and the third CSI report, and the transmission of the CSI report transmission is based on omitting a portion of the third CSI report from the CSI report transmission.

[0148] Example 9 may be combined with any one of Examples 1 to 8, and includes the CSI report transmission comprising an indication to omit the at least first portion of the first CSI report from the CSI report transmission.

[0149] Example 10 can be combined with any one of Examples 1 to 9, and further includes receiving a configuration from the network entity for omitting at least the first part of the first CSI report from the CSI report transmission when the total payload size of the CSI report transmission exceeds the threshold.

[0150] Example 11 can be combined with any one of Examples 1 to 10, and further includes receiving a trigger indication for the first CSI report from the network entity, wherein when the total payload size of the CSI report transmission exceeds the threshold, at least the first part of the first CSI report is omitted from the CSI report transmission.

[0151] Example 12 is a method for performing wireless communications at a network entity, comprising: transmitting a CSI-RS associated with a first CSI report to a UE; and receiving a CSI report transmission from the UE, wherein based on a total payload size of the CSI report transmission exceeding a threshold, at least a first portion of the first CSI report is omitted from the CSI report transmission.

[0152] Example 13 may be combined with Example 12, and include that the CSI report transmission includes a plurality of CSI reports, the plurality of CSI reports including at least one of: an ML-based CSI report or a non-ML-based CSI report.

[0153] Example 14 can be combined with any one of Examples 12 to 13, and includes omitting the at least first part of the first CSI report from the CSI report transmission based on the at least first part of the first CSI report having a lower priority than the second part of the first CSI report.

[0154] Example 15 may be combined with Example 14 and include that the first CSI report is the ML-based CSI report.

[0155] Example 16 may be combined with Example 14, and include that the first CSI report is the non-ML based CSI report.

[0156] Example 17 can be combined with any one of Examples 12 to 13, and includes that the first CSI report is the ML-based CSI report, and includes omitting the at least first part of the first CSI report from the CSI report transmission including: replacing the first CSI report with a second CSI report, the second CSI report being the non-ML-based CSI report.

[0157] Example 18 may be combined with Example 17 and include replacing the first CSI report with the second CSI report is based on the first CSI report having a priority level lower than a threshold priority level.

[0158] Example 19 may be combined with any one of Examples 17 to 18, and includes the CSI report transmission having the multiple CSI reports, and including the second CSI report and the third CSI report, and includes receiving the CSI report transmission including omitting a portion of the third CSI report from the CSI report transmission.

[0159] Example 20 may be combined with any one of Examples 12 to 19, and include the CSI report transmission including an indication to omit the at least first portion of the first CSI report from the CSI report transmission.

[0160] Example 21 can be combined with any one of Examples 12 to 20, and further includes transmitting to the UE a configuration for omitting at least the first part of the first CSI report from the CSI report transmission when the total payload size of the CSI report transmission exceeds the threshold.

[0161] Example 22 can be combined with any one of Examples 12 to 21, and further includes transmitting a trigger indication for the first CSI report to the UE, wherein when the total payload size of the CSI report transmission exceeds the threshold, at least the first part of the first CSI report is omitted from the CSI report transmission.

[0162] Example 23 can be combined with any one of Examples 12 to 22, and further includes decoding the CSI report transmission based on an indication to omit the at least first portion of the first CSI report from the CSI report transmission.

[0163] Example 24 is a device for wireless communication implementing the method described in any one of Examples 1 to 23.

[0164] Example 25 is an apparatus for wireless communication including means for implementing a method as described in any one of Examples 1 to 23.

[0165] Example 26 is a non-transitory computer-readable medium storing computer executable code, which, when executed by at least one processor, causes the at least one processor to implement a method as described in any one of Examples 1 to 23.

Claims

1. A method for wireless communication at a user equipment UE (102), comprising: receiving (306) a channel state information-reference signal CSI-RS associated with a first channel state information CSI report from a network entity (104); as well as A CSI report transmission is transmitted (310) to the network entity (104), wherein at least a first portion of the first CSI report is omitted (308) from the CSI report transmission when a total payload size of the CSI report transmission exceeds a threshold.

2. The method of claim 1, wherein the CSI report transmission comprises a plurality of CSI reports, and the plurality of CSI reports comprises at least one of: a CSI report based on machine learning (ML) or a CSI report based on non-ML.

3. The method of any one of claims 1 to 2, wherein omitting (308a) the at least first part of the first CSI report from the CSI report transmission is based on the at least first part of the first CSI report having a lower priority than the second part of the first CSI report.

4. The method of claim 3, wherein the first CSI report comprises the ML-based CSI report.

5. The method of claim 3, wherein the first CSI report comprises the non-ML based CSI report.

6. The method of any one of claims 1 to 2, wherein the first CSI report comprises the ML-based CSI report, and wherein omitting (308b) the at least first portion of the first CSI report from the CSI report transmission further comprises: The first CSI report in the CSI report transmission is replaced (308b) with a second CSI report, the second CSI report including the non-ML based CSI report.

7. The method of claim 6, wherein replacing (308b) the first CSI report with the second CSI report is based on the first CSI report having a priority level below a threshold priority level.

8. The method of any one of claims 6 to 7, wherein the CSI report transmission has the multiple CSI reports and includes the second CSI report and a third CSI report, and transmitting (310) the CSI report transmission is based on omitting a portion of the third CSI report from the CSI report transmission.

9. The method according to any one of claims 1 to 8, wherein the CSI report transmission comprises an indication to omit (308) the at least first part of the first CSI report from the CSI report transmission.

10. The method according to any one of claims 1 to 9, further comprising: A configuration is received (302) from the network entity (104) for omitting (308) the at least first portion of the first CSI report from the CSI report transmission when the total payload size of the CSI report transmission exceeds the threshold.

11. The method of any one of claims 1 to 10, further comprising: A trigger indication for the first CSI report is received (304) from the network entity (104), wherein the at least a first portion of the first CSI report is omitted (308) from the CSI report transmission when the total payload size of the CSI report transmission exceeds the threshold.

12. A method for wireless communication at a network entity (104), comprising: transmitting (306) a channel state information-reference signal CSI-RS associated with a first channel state information CSI report to a user equipment UE (102); as well as A CSI report transmission is received (310) from the UE (102), wherein at least a first portion of the first CSI report is omitted from the CSI report transmission based on a total payload size of the CSI report transmission exceeding a threshold.

13. The method of claim 12, wherein omitting the at least first portion of the first CSI report from the CSI report transmission is based on the at least first portion of the first CSI report having a lower priority than the second portion of the first CSI report.

14. The method of claim 12, wherein the first CSI report comprises a machine learning (ML) based CSI report, and wherein omitting the at least a first portion of the first CSI report from the CSI report transmission comprises replacing the first CSI report with a second CSI report, the second CSI report comprising a non-ML based CSI report.

15. The method of claim 1, further comprising: Based on an indication to omit the at least first portion of the first CSI report from the CSI report transmission, the CSI report transmission is decoded (312).

16. An apparatus for wireless communication, comprising a memory, a transceiver and a processor, the processor being coupled to the memory and the transceiver, the apparatus being configured to implement the method according to any one of claims 1 to 15.

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