Method and apparatus for beam management operation in wireless networks

Abstract

A method performed by a User Equipment (UE) for performing a beam management operation is provided. The method receives, from a Base Station (BS), a first Channel State Information (CSI) report configuration and a second CSI report configuration for configuring a first CSI report and a second CSI report, respectively. The first CSI report configuration includes an indicator for indicating that the first CSI report is for Artificial Intelligence (AI) / Machine Learning (ML) purpose. The method determines a first priority for the first CSI report based on a first parameter associated with the first RRC configuration. The method determines a second priority for the second CSI report based on a second parameter associated with the second RRC configuration. The method determines that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.　

Description

METHOD AND APPARATUS FOR BEAM MANAGEMENT OPERATION IN WIRELESS NETWORKS

[0001] The present disclosure is related to wireless communication and, more specifically, to a User Equipment (UE), Base Station (BS), and method for a beam management operation in the wireless communication networks.

[0002] Various efforts have been made to improve different aspects of wireless communication for the cellular wireless communication systems, such as the 5thGeneration (5G) New Radio (NR), by improving data rate, latency, reliability, and mobility. The 5G NR system is designed to provide flexibility and configurability to optimize network services and types, accommodating various use cases, such as enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable and Low-Latency Communication (URLLC). As the demand for radio access continues to grow, however, there exists a need for further improvements in the next-generation wireless communication systems, such as improvements in a beam management procedure.

[0003] The present disclosure is related to a UE, a BS, and a method for a beam management operation in the wireless communication networks.

[0004] In a first aspect of the present disclosure, a UE for performing a beam management operation is provided. The UE includes at least one processor and at least one non-transitory computer-readable medium that is coupled to the at least one processor and that stores one or more computer-executable instructions. The computer-executable instructions, when executed by the at least one processor, cause the UE to: receive, from a BS, a first Channel State Information (CSI) report configuration for configuring a first CSI report, the first CSI report configuration including an indicator for indicating that the first CSI report is for Artificial Intelligence (AI) / Machine Learning (ML) purpose; receive, from the BS, a second CSI report configuration for configuring a second CSI report; determine a first priority for the first CSI report based on at least one first parameter associated with the first RRC configuration; determine a second priority for the second CSI report based on at least one second parameter associated with the second RRC configuration; and determine that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

[0005] In some implementations of the first aspect, the first priority corresponds to a first priority value, the second priority corresponds to a second priority value, and the first priority value is greater than the second priority value.

[0006] In some implementations of the first aspect, the one or more computer-executable instructions, when executed by the at least one processor, further cause the UE to transmit, to the BS, the second CSI report and drop the first CSI report in response to determining that the first CSI report and the second CSI report overlap in time domain.

[0007] In some implementations of the first aspect, the one or more computer-executable instructions, when executed by the at least one processor, further cause the UE to transmit, to the BS, the second CSI report and a portion of the first CSI report on a Physical Uplink Shared Channel (PUSCH) in response to determining that the first CSI report and the second CSI report overlap in time domain.

[0008] In some implementations of the first aspect, the at least one first parameter includes a first report configuration identifier (ID) included in the first CSI report configuration, and the at least one second parameter includes a second report configuration ID included in the second CSI report configuration.

[0009] In some implementations of the first aspect, the at least one first parameter includes a first data type indicator that indicates a first content type of the first CSI report, and the at least one second parameter includes a second data type indicator that indicates a second content type of the second CSI report.

[0010] In some implementations of the first aspect, the first CSI report includes a first beam report of predicted results, and the second CSI report includes a second beam report of measurement results.

[0011] In a second aspect of the present application, a BS for configuring a beam management operation is provided. The BS includes at least one processor and at least one non-transitory computer-readable medium that is coupled to the at least one processor and that stores one or more computer-executable instructions. The computer-executable instructions, when executed by the at least one processor, cause the BS to: transmit, to a UE, a first CSI report configuration for configuring a first CSI report, the first CSI report configuration including an indicator for indicating that the first CSI report is for AI / ML purpose; and transmit, to the UE, a second CSI report configuration for configuring a second CSI report. A first priority for the first CSI report is based on at least one first parameter associated with the first RRC configuration. A second priority for the second CSI report is based on at least one second parameter associated with the second RRC configuration. The UE determines that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

[0012] In some implementations of the second aspect, the first priority corresponds to a first priority value, the second priority corresponds to a second priority value, and the first priority value is greater than the second priority value.

[0013] In some implementations of the second aspect, the one or more computer-executable instructions, when executed by the at least one processor, further cause the BS to receive, from the UE, the second CSI report. The UE determines to transmit, to the BS, the second CSI report and drop the first CSI report in response to determining that the first CSI report and the second CSI report overlap in time domain.

[0014] In some implementations of the second aspect, the one or more computer-executable instructions, when executed by the at least one processor, further cause the BS to receive, from the UE, the second CSI report and a portion of the first CSI report on a PUSCH in a case that the first CSI report and the second CSI report overlap in time domain.

[0015] In some implementations of the second aspect, the at least one first parameter includes a first report configuration ID included in the first CSI report configuration, and the at least one second parameter includes a second report configuration ID included in the second CSI report configuration.

[0016] In some implementations of the second aspect, the at least one first parameter includes a first data type indicator that indicates a first content type of the first CSI report, and the at least one second parameter includes a second data type indicator that indicates a second content type of the second CSI report.

[0017] In some implementations of the second aspect, the first CSI report includes a first beam report of predicted results, and the second CSI report includes a second beam report of measurement results.

[0018] In a third aspect of the present application, a method performed by a UE for performing a beam management operation is provided. The method includes receiving, from a BS, a first CSI report configuration for configuring a first CSI report, the first CSI report configuration including an indicator for indicating that the first CSI report is for AI / ML purpose; receiving, from the BS, a second CSI report configuration for configuring a second CSI report; determining a first priority for the first CSI report based on at least one first parameter associated with the first RRC configuration; determining a second priority for the second CSI report based on at least one second parameter associated with the second RRC configuration; and determining that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

[0019] Aspects of the present disclosure are best understood from the following detailed disclosure when read with the accompanying drawings. Various features are not drawn to scale. Dimensions of various features may be arbitrarily increased or reduced for clarity of discussion.

[0020] FIG. 1 is a block diagram illustrating a functional framework for AI / ML for NR air interface, according to an example implementation of the present disclosure.

[0021] FIG. 2 is a flowchart illustrating a method / process performed by a UE for performing a beam management operation, according to an example implementation of the present disclosure.

[0022] FIG. 3 is a flowchart illustrating a method / process performed by a BS for configuring a beam management operation, according to an example implementation of the present disclosure.

[0023] FIG. 4 is a block diagram illustrating a node for wireless communication, according to an example implementation of the present disclosure.

[0024] Some of the abbreviations used in the present disclosure include: 3GPP　　　　　　　　3rd Generation Partnership Project 5G　　　　　　　　5th generation ACK　　　　　　　　Acknowledgment AI　　　　　　　　Artificial Intelligence AL　　　　　　　　Aggregation level AP　　　　　　　　Aperiodic BFD　　　　　　　　Beam Failure Detection BM　　　　　　　　Beam Management BS　　　　　　　　Base Station BWP　　　　　　　　Bandwidth Part CA　　　　　　　　Carrier Aggregation CORESET 　　　　　　　　Control resource set CC　　　　　　　　Component Carrier CCE　　　　　　　　Control Chanel Element CE　　　　　　　　Control Element CG　　　　　　　　Configured Grant CJT　　　　　　　　Coherent Joint Transmission CP　　　　　　　　Cyclic Prefix CRC　　　　　　　　Cyclic Redundancy Check CRI　　　　　　　　CSI-RS Resource Indicator CSI　　　　　　　　Channel State Information CSI-RS　　　　　　　　Channel State Information-Reference Signal DC　　　　　　　　Dual Connectivity DCI　　　　　　　　Downlink Control Information DL　　　　　　　　Downlink DMRS　　　　　　　　Demodulation Reference Signal FR　　　　　　　　Frequency Range HARQ　　　　　　　　Hybrid Automatic Repeat Request ID　　　　　　　　Identifier IE　　　　　　　　Information Element LSB　　　　　　　　Least Significant Bit LTE　　　　　　　　Long Term Evolution L1 / L2 / L3　　　　　　　　Layer 1 / Layer 2 / Layer 3 L1-RSRP　　　　　　　　Layer 1 Reference Signal Received Power LRR　　　　　　　　Link Recovery Request MAC　　　　　　　　Medium Access Control MCG　　　　　　　　Master Cell Group MIMO　　　　　　　　Multiple-input Multiple-output MSB　　　　　　　　Most Significant Bit ML　　　　　　　　Machine Learning NACK　　　　　　　　Negative Acknowledgment NDI　　　　　　　　New Data Indicator NR　　　　　　　　New RAT / Radio NW　　　　　　　　Network NUL　　　　　　　　Normal UL PBCH　　　　　　　　Physical Broadcast Channel PCI　　　　　　　　Physical Cell ID PCell　　　　　　　　Primary Cell PDCCH　　　　　　　　Physical Downlink Control Channel PDSCH　　　　　　　　Physical Downlink Shared Channel PDU　　　　　　　　Protocol Data Unit PHY　　　　　　　　Physical PMI　　　　　　　　Precoding Matrix Indicator PRACH　　　　　　　　Physical Random Access Channel PSCell　　　　　　　　Primary SCG Cell PTRS　　　　　　　　Phase-Tracking Reference Signal PUCCH　　　　　　　　Physical Uplink Control Channel PUSCH　　　　　　　　Physical Uplink Shared Channel QoS　　　　　　　　Quality of Service RA　　　　　　　　Random Access RAN　　　　　　　　Radio Access Network RAR　　　　　　　　Random Access Response RAT　　　　　　　　Radio Access Technology Rel　　　　　　　　Release RNTI　　　　　　　　Radio Network Temporary Identifier RRC　　　　　　　　Radio Resource Control RS　　　　　　　　Reference Signal RSRP　　　　　　　　Reference Signal Received Power RSRQ　　　　　　　　Reference Signal Received Quality RV　　　　　　　　Redundancy Version SCell　　　　　　　　Secondary Cell SCG　　　　　　　　Secondary Cell Group SCS　　　　　　　　Subcarrier Spacing SINR　　　　　　　　Signal to Interference plus Noise Ratio SpCell　　　　　　　　Special Cell SR　　　　　　　　Scheduling Request SRS　　　　　　　　Sounding Reference Signal SS　　　　　　　　Synchronization Signal SSB　　　　　　　　Synchronization Signal Block SSBRI　　　　　　　　SS / PBCH Block Resource indicator SUL　　　　　　　　Supplementary UL TA　　　　　　　　Timing Advance TAG　　　　　　　　Timing Advance Group TB　　　　　　　　Transport Block TCI　　　　　　　　Transmission Configuration Indication TDCP　　　　　　　　Time Domain Channel Properties TR　　　　　　　　Technical Report TRP　　　　　　　　Transmission Reception Point TS　　　　　　　　Technical Specification Tx　　　　　　　　Transmission QCL　　　　　　　　Quasi-CoLocation UE　　　　　　　　User Equipment UL　　　　　　　　Uplink URLLC　　　　　　　　Ultra Reliable Low Latency Communication

[0025] The following contains specific information related to implementations of the present disclosure. The drawings and their accompanying detailed disclosure are merely directed to implementations. However, the present disclosure is not limited to these implementations. Other variations and implementations of the present disclosure will be obvious to those skilled in the art.

[0026] Unless noted otherwise, like or corresponding elements among the drawings may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present disclosure are generally not to scale and are not intended to correspond to actual relative dimensions.

[0027] For the purposes of consistency and ease of understanding, like features may be identified (although, in some examples, not illustrated) by the same numerals in the drawings. However, the features in different implementations may be different in other respects and may not be narrowly confined to what is illustrated in the drawings.

[0028] References to “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” “implementations of the present application,” etc., may indicate that the implementation(s) of the present application so described may include a particular feature, structure, or characteristic, but not every possible implementation of the present application necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “In some implementations,” or “in an example implementation,” “an implementation,” do not necessarily refer to the same implementation, although they may. Moreover, any use of phrases like “implementations” in connection with “the present application” are never meant to characterize that all implementations of the present application must include the particular feature, structure, or characteristic, and should instead be understood to mean “at least some implementations of the present application” includes the stated particular feature, structure, or characteristic. The term “coupled” is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The term “comprising,” when utilized, means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in the so-described combination, group, series, and the equivalent.

[0029] The expression “at least one of A, B and C” or “at least one of the following: A, B and C” means “only A, or only B, or only C, or any combination of A, B and C.” The terms “system” and “network” may be used interchangeably. The term “and / or” is only an association relationship for describing associated objects and represents that three relationships may exist such that A and / or B may indicate that A exists alone, A and B exist at the same time, or B exists alone. The character “ / ” generally represents that the associated objects are in an “or” relationship.

[0030] For the purposes of explanation and non-limitation, specific details, such as functional entities, techniques, protocols, and standards, are set forth for providing an understanding of the disclosed technology. In other examples, detailed disclosure of well-known methods, technologies, systems, and architectures are omitted so as not to obscure the present disclosure with unnecessary details.

[0031] Persons skilled in the art will immediately recognize that any network function(s) or algorithm(s) disclosed may be implemented by hardware, software, or a combination of software and hardware. Disclosed functions may correspond to modules which may be software, hardware, firmware, or any combination thereof.

[0032] A software implementation may include computer executable instructions stored on a computer-readable medium, such as memory or other type of storage devices. One or more microprocessors or general-purpose computers with communication processing capability may be programmed with corresponding executable instructions and perform the disclosed network function(s) or algorithm(s).

[0033] The microprocessors or general-purpose computers may include Application-Specific Integrated Circuits (ASICs), programmable logic arrays, and / or one or more Digital Signal Processor (DSPs). Although some of the disclosed implementations are oriented to software installed and executing on computer hardware, alternative implementations implemented as firmware, as hardware, or as a combination of hardware and software are well within the scope of the present disclosure. The computer-readable medium includes but is not limited to Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, Compact Disc Read-Only Memory (CD-ROM), magnetic cassettes, magnetic tape, magnetic disk storage, or any other equivalent medium capable of storing computer-readable instructions.

[0034] A radio communication network architecture such as a Long-Term Evolution (LTE) system, an LTE-Advanced (LTE-A) system, an LTE-Advanced Pro system, or a 5G NR Radio Access Network (RAN) typically includes at least one base station (BS), at least one UE, and one or more optional network elements that provide connection within a network. The UE communicates with the network such as a Core Network (CN), an Evolved Packet Core (EPC) network, an Evolved Universal Terrestrial RAN (E-UTRAN), a 5G Core (5GC), or an internet via a RAN established by one or more BSs.

[0035] A UE may include, but is not limited to, a mobile station, a mobile terminal or device, or a user communication radio terminal. The UE may be a portable radio equipment that includes, but is not limited to, a mobile phone, a tablet, a wearable device, a sensor, a vehicle, or a Personal Digital Assistant (PDA) with wireless communication capability. The UE is configured to receive and transmit signals over an air interface to one or more cells in a RAN.

[0036] The BS may be configured to provide communication services according to at least a Radio Access Technology (RAT) such as Worldwide Interoperability for Microwave Access (WiMAX), Global System for Mobile communications (GSM) that is often referred to as 2G, GSM Enhanced Data rates for GSM Evolution (EDGE) RAN (GERAN), General Packet Radio Service (GPRS), Universal Mobile Telecommunication System (UMTS) that is often referred to as 3G based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), LTE, LTE-A, evolved LTE (eLTE) that is LTE connected to 5GC, NR (often referred to as 5G), and / or LTE-A Pro. However, the scope of the present disclosure is not limited to these protocols.

[0037] The BS may include, but is not limited to, a node B (NB) in the UMTS, an evolved node B (eNB) in LTE or LTE-A, a radio network controller (RNC) in UMTS, a BS controller (BSC) in the GSM / GERAN, an ng-eNB in an Evolved Universal Terrestrial Radio Access (E-UTRA) BS in connection with 5GC, a next generation Node B (gNB) in the 5G-RAN, or any other apparatus capable of controlling radio communication and managing radio resources within a cell. The BS may serve one or more UEs via a radio interface. Although the gNB is used as an example in some implementations within the present disclosure, it should be noted that the disclosed implementations may also be applied to other types of base stations.

[0038] The BS may be operable to provide radio coverage to a specific geographical area using multiple cells forming the RAN. The BS may support the operations of the cells. Each cell may be operable to provide services to at least one UE within its radio coverage.

[0039] Each cell (may often referred to as a serving cell) may provide services to one or more UEs within the cell’s radio coverage, such that each cell schedules the DL (and optionally UL resources) to at least one UE within its radio coverage for DL (and optionally UL packet transmissions from the UE). The BS may communicate with one or more UEs in the radio communication system via the cells.

[0040] A cell may allocate sidelink (SL) resources for supporting the Proximity Services (ProSe) or Vehicle to Everything (V2X) services. Each cell may have overlapped coverage areas with other cells.

[0041] In Multi-RAT Dual Connectivity (MR-DC) cases, the primary cell of a Master Cell Group (MCG) or a Secondary Cell Group (SCG) may be referred to as a Special Cell (SpCell). A Primary Cell (PCell) may include the SpCell of an MCG. A Primary SCG Cell (PSCell) may include the SpCell of an SCG. MCG may include a group of serving cells associated with the Master Node (MN), including the SpCell and optionally one or more Secondary Cells (SCells). An SCG may include a group of serving cells associated with the Secondary Node (SN), including the SpCell and optionally one or more SCells.

[0042] As discussed above, the frame structure for NR may support flexible configurations for accommodating various next generation (e.g., 5G) communication requirements, such as Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), and Ultra-Reliable and Low-Latency Communication (URLLC), while fulfilling high reliability, high data rate, and low latency requirements. The Orthogonal Frequency-Division Multiplexing (OFDM) technology in the 3GPP may serve as a baseline for an NR waveform. The scalable OFDM numerology, such as adaptive sub-carrier spacing, channel bandwidth, and Cyclic Prefix (CP), may also be used.

[0043] Two coding schemes may be considered for NR, specifically, Low-Density Parity-Check (LDPC) code and Polar Code. The coding scheme adaption may be configured based on channel conditions and / or service applications.

[0044] At least the DL transmission data, a guard period, and UL transmission data should be included in a transmission time interval (TTI) of a single NR frame. The respective portions of the DL transmission data, the guard period, and the UL transmission data should also be configurable based on, for example, the network dynamics of NR. SL resources may also be provided in an NR frame to support ProSe services or V2X services.

[0045] Any two or more than two of the following paragraphs, (sub)-bullets, points, actions, behaviors, terms, or claims described in the present disclosure may be combined logically, reasonably, and properly to form a specific method.

[0046] Any sentence, paragraph, (sub)-bullet, point, action, behaviors, terms, or claims described in the present disclosure may be implemented independently and separately to form a specific method.

[0047] Dependency, e.g., “based on”, “more specifically”, “preferably”, “in one embodiment”, “in some implementations”, etc., in the present disclosure is just one possible example which would not restrict the specific method.

[0048] In some implementations, all the designs / embodiment / implementations introduced within this disclosure are not limited to be applied for dealing with the problems discussed within this disclosure. For example, the described embodiments may be applied to solve other problems that exist in the RAN of wireless communication systems. In some implementations, all of the numbers listed within the designs / embodiment / implementations introduced within this disclosure are just examples and for illustration, for example, of how the described methods are executed.

[0049] Examples of some selected terms in the present disclosure are provided as follows.

[0050] DCI: DCI may include downlink control information, and there may be various DCI formats used in a PDCCH. The DCI format may be a predefined format in which the downlink control information may be packed / formed and transmitted in a PDCCH.

[0051] BWP: A subset of the total cell bandwidth of a cell is referred to as a Bandwidth Part (BWP) and a Bandwidth Adaptation (BA) may be achieved by configuring the UE with BWP(s) and instructing the UE which of the configured BWPs is currently the active one. To enable a BA on the PCell, the BS (e.g., a gNB) configures the UE with UL and DL BWP(s). To enable the BA on SCells, when CA is deployed, the BS configures the UE with one or more DL BWPs. It should be noted that there may be no BWP in the UL. For the PCell, the initial BWP is the BWP used for an initial access. For the SCell(s), the initial BWP is the BWP configured for the UE to operate after an SCell activation. The UE may be configured with a first active uplink BWP by a firstActiveUplinkBWP IE. If the first active uplink BWP is configured for an SpCell, the firstActiveUplinkBWP IE field may contain the ID of the UL BWP to be activated upon performing the RRC (re-)configuration. If such a field is absent, the RRC (re-)configuration may not impose a BWP switching. If the first active uplink BWP is configured for an SCell, the firstActiveUplinkBWP IE field may contain the ID of the uplink bandwidth part to be used upon the MAC-activation of an SCell.

[0052] TCI state: A TCI state may include parameters for configuring a QCL relationship between one or more DL reference signals and a target reference signal set. For example, a target reference signal set may include the DMRS ports of a PDSCH, a PDCCH, a PUCCH, or a PUSCH. The reference signals may include UL or DL reference signals. In NR Rel-15 / 16, the TCI state may be used for a DL QCL indication, whereas the spatial relation information may be used for providing the UL spatial transmission filter information for the UL signal(s) or channel(s). A TCI state may include the information similar to the spatial relation information, which may be used for UL transmission. In other words, from the UL perspective, a TCI state may provide the UL beam information that may indicate the relationship between a UL transmission and the DL or UL reference signals (e.g., the CSI-RS, the SSB, the SRS, and the PTRS).

[0053] Beam: The term “beam” here may be replaced by a spatial filter. A beam may correspond to a spatial (domain) filtering. In one example, the spatial filtering may be applied in the analog domain by adjusting a phase and / or an amplitude of a signal before being transmitted by a corresponding antenna element. In another example, the spatial filtering may be applied in the digital domain by the Multi-Input Multi-Output (MIMO) technique in the wireless communication system. For example, “a UE made a PUSCH transmission by using a specific beam” may imply that the UE made the PUSCH transmission by using the specific spatial / digital domain filter. The “beam” may also be, but is not limited to be, represented as an antenna, an antenna port, an antenna element, a group of antennas, a group of antenna ports, or a group of antenna elements. The beam may also be formed by a certain reference signal resource. In short, the beam may be equivalent to a spatial domain filter through which the electromagnetic (EM) wave is radiated.

[0054] Cell: A cell in the present disclosure may refer to a PCell, a PSCell, an SpCell, an SCell, a candidate cell, a target cell, a neighbor cell, a serving cell, or a source cell.

[0055] The ‘TRP’ in the implementations or the examples may be replaced by ‘beam’ or ‘panel’. The term ‘overlap’ may refer to partial overlap or fully overlap in time domain, frequency domain and / or spatial domain.

[0056] In the realm of wireless communication, beam management (BM) plays a crucial role in optimizing the performance of communication networks, particularly in the context of emerging technologies like 5G and beyond. Beam management involves the selection, tracking, and optimization of beams in a beamforming system to ensure efficient and reliable data transmission between base stations and user devices. Furthermore, AI / ML-driven beam management enhances spectral efficiency by dynamically adjusting beamforming parameters to optimize resource allocation and / or by maximizing throughput per unit of spectrum. It improves coverage by intelligently steering beams towards active users and high-demand areas, ensuring better signal propagation and reduced dead zones. Additionally, AI / ML algorithms mitigate interference, enhance signal quality and reliability, thereby improving overall quality of service (QoS). Through these mechanisms (e.g., AI / ML algorithms), AI / ML-driven beam management optimizes spectrum usage, extends coverage, and enhances QoS, leading to more efficient and reliable wireless communication systems.

[0057] For CSI report for AI / ML BM, a dedicated CSI resource set may be configured. The associated CSI report configuration may be indicated as being for the AI / ML beam report purpose. In some implementations, a first CSI report for beam report of predicted results and a second CSI report for beam report of measurement results may not be transmitted within the same CSI report. In some implementations, the UE may transmit the second CSI report for beam report of measurement results and drop the first CSI report for beam report of predicted results in response to determining that the first and second CSI reports overlap in time domain. In some implementations, two CSI reports for different purposes may not be multiplexed together due to different reliability requirements and different latency requirements. The present disclosure addresses implementations regarding how to determine CSI priority for the overlapping CSI reports with different purposes.

[0058] In some implementations, a method for AI / ML based beam management performed by a UE may include the following actions: receiving a first RRC configuration to configure a set of CSI-RS resources associated with an indicator and a first CSI report; receiving a second RRC configuration to configure a set of CSI-RS resources associated with a second CSI report; and transmitting the first CSI report based on a priority value. The priority value may include a parameter to make the priority value for the first CSI report lower than the priority value for the second CSI report, and the indicator may indicate the set of CSI-RS resources is for the AI / ML purpose. In some implementations, a lower priority value may correspond to a higher priority. For example, the first CSI report with a priority value 1 may have a higher priority than the second CSI report with a priority value 2. In some implementations, a lower priority value may correspond to a lower priority. For example, the first CSI report with a priority value 1 may have a lower priority than the second CSI report with a priority value 2.

[0059] In some implementations, the first CSI report may be transmitted on a PUSCH and the second CSI report may be transmitted on a PUCCH.

[0060] Functional framework for AI / ML for NR air interface

[0061] FIG. 1 is a block diagram 100 illustrating a functional framework for AI / ML for NR air interface, according to an example implementation of the present disclosure. As illustrated in FIG. 1, data collection 102 is a function that provides input data to the model training, management, and inference functions. Training data may include data needed as input for the AI / ML model training function. Monitoring data may include data needed as input for the management of AI / ML models or AI / ML functionalities. Inference data may include data needed as input for the AI / ML inference function.

[0062] Model training 104 is a function that performs AI / ML model training, validation, and testing, which may generate model performance metrics that may be used as part of the model testing procedure. The model training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on training data delivered by a data collection function, if required. In some implementations, there may be a model storage 106 in the framework. The model training 104 may deliver a trained / updated model to the model storage 106. In some implementations, the trained / updated model may include trained, validated, and tested AI / ML models. In some implementations, the trained / updated model may include an updated version of the model.

[0063] Management 108 is a function that oversees the operation (e.g., selection / (de)activation / switching / fallback) and monitoring (e.g., performance) of AI / ML models or AI / ML functionalities. The management 108 is also responsible for making decisions to ensure the proper inference operation based on data received from the data collection function and the inference function. In some implementations, a management instruction represents essential input information to manage the inference function. Concerning information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functionalities, fallback to non-AI / ML operation (e.g., not relying on inference process), and so on. In some implementations, a model transfer / delivery request is used to request model(s) to the model storage 106. In some implementations, performance feedback and retraining request represent essential input information for the model training 104 (e.g., for model (re)training or updating purposes).

[0064] Inference 110 is a function that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the data collection 102 (e.g., inference data) as an input. The inference 110 is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the inference data delivered by the data collection 102, if required. In some implementations, inference output serves as data for the management 108 to monitor the performance of AI / ML models or AI / ML functionalities.

[0065] Model storage 106 is a function responsible for storing trained / updated models that may be used to perform the inference function. The model storage 106 may transfer / deliver an AI / ML model to the inference 110.

[0066] L1 measurement and report

[0067] A UE may perform, and report measurement (e.g., L1 measurement or L3 measurement) based on the received configuration and / or indication. L1 measurements may be further classified into L1 intra-frequency measurement, or L1 inter-frequency measurement. In some implementations, L1 intra-frequency measurement and L1 inter-frequency measurement may be based on L1-RSRP through measuring SSB (e.g., SS-RSRP) or CSI-RS (e.g., CSI-RSRP). In some implementations, L1 intra-frequency measurement and L1 inter-frequency measurement may be based on L1-SINR through measuring SSB (e.g., SS-SINR) or CSI-RS (CSI-SINR). In some implementations, L1 intra-frequency measurement and L1 inter-frequency measurement may be based on L1-RSRQ through measuring SSB (e.g., SS-RSRQ) or CSI-RS (e.g., CSI-RSRQ).

[0068] In some implementations, the L1 measurement report may include one or some PCIs (e.g., PCIs of the candidate cells, PCI of the source cell, PCI of the serving cell, or PCI of the target cell). In some implementations, the L1 measurement report may include one or some RS ID.

[0069] In some implementations, an L1 measurement report, transmitted as UCI on PUCCH or PUSCH, may be considered as the result of measurement from the UE’s perspective. In some implementations, the types of L1 measurement reports may include a periodic report on PUCCH, a semi-persistent report on PUCCH or PUSCH, and an aperiodic report on PUSCH. In some implementations, the L1 measurement report may be transmitted on a MAC-CE.

[0070] CSI measurement configuration

[0071] CSI measurement configuration involves setting parameters that configure how CSI measurements are performed and reported by a UE. The CSI measurement configuration configures a list of non-zero power CSI-RS resources for channel measurement, a list of CSI-RS resources for interference management, and a list of SSB resources for CSI measurement and reporting. In some implementation, the CSI measurement configuration may be associated with a CSI report configuration. In some implementations, when the CSI resources in the CSI resource set refer to beam measurement, the corresponding CSI report may only contain CRI, SSBRI, L1-RSRP, L1-SINR for each measurement result based on the configured CSI-RS resources, and one CSI-RS resource or SSB may correspond to one L1-RSRP / L1-SINR / L1-RSRQ value.

[0072] CSI report content

[0073] In some implementations, a CSI report may include Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS resource indicator (CRI), SS / PBCH block resource indicator (SSBRI), Layer Indicator (LI), Rank Indicator (RI), Capability Index, L1-RSRP, L1-SINR, and / or L1-RSRQ. In some implementations, the report may contain top K values among a set of measurement results by measuring the CSI-RS resource or SSB, where K is a positive integer.

[0074] AI / ML-based beam management

[0075] The beam management may include DL Tx beam prediction for both the UE-sided model and the NW-sided model. The beam prediction may include predicting a Set A of beams based on a Set B of beams and an AI / ML model. For example, the AI / ML model may receive the Set B of beams as input and generate the Set A of beams as output. In some implementations, the measurement results may correspond to the inference / predicted results.

[0076] BM-Case1: BM-Case1 may refer to a spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams. In some implementations, the Set B of beams may include beams received from one direction and the Set A of beams may include beams received from another direction. In some implementations, the Set B of beams may include wide beams (e.g., SSB) and the Set A of beams may include narrow beams (e.g., CSI-RS). In some implementations, the AI / ML model training and inference may be performed at the NW side. In some implementations, the AI / ML model training and inference may be performed at the UE side. In some implementations, Set A and Set B may be different. In some implementations, Set B may be a subset of Set A.

[0077] BM-Case2: BM-Case2 may refer to a temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams. In some implementations, the Set B of beams may include beams that were received in the past and the Set A of beams may include beams that are expected to be received in the future. In some implementations, the AI / ML model training and inference may be performed at the NW side. In some implementations, the AI / ML model training and inference may be performed at the UE side. In some implementations, Set A and Set B may be different. In some implementations, Set B may be a subset of Set A. In some implementations, Set A and Set B may be the same.

[0078] Implementations

[0079] In some implementations, an RRC message (e.g., transmitted from the NW to the UE) may configure a CSI measurement configuration including one or more CSI-RS resource sets / CSI-SSB resource sets. Each CSI-RS resource set / CSI-SSB resource set may include one or more CSI-RS resource indexes / SSB resource indexes. Furthermore, the CSI-RS resource set / CSI-SSB resource set may be associated with a CSI report configuration, and the CSI-RS resource set / CSI-SSB resource set may be dedicated to the AI / ML purpose. In addition, a CSI resource configuration index may be used to configure the CSI resources for the measurement purpose and another CSI resource configuration index may be used to configure the CSI resources for the prediction purpose.

[0080] In some implementations, when an indicator is configured to a CSI-RS resource set, the UE may transmit a CSI report for AI / ML based on the CSI-RS resource set and the indicator. In some implementations, the indicator may be configured to a UE for determining whether the corresponding CSI-RS resource set(s) / CSI-RS resource(s) are used for AI / ML (e.g., an AI / ML-specific indicator). In some implementations, the indicator may be used to determine whether the corresponding CSI-RS resource set(s) is included in the configuration(s) of the corresponding CSI-RS resource set(s). In some implementations, the indicator may be used to determine whether the corresponding CSI-RS resource(s) is included in the configuration(s) of the corresponding CSI-RS resource(s). In some implementations, the indicator may be an IE taking ENUMERATED value from the set {‘true’} or {‘true’, ‘false’}. If the indicator is present and set to ‘true’, the UE may interpret that the CSI-RS resource set is associated with a CSI report for AI / ML. If the indicator is absent or present with a value of ‘false’, the UE may interpret that the CSI-RS resource set is associated with a CSI report other than for AI / ML.

[0081] In some implementations, a CSI-RS resource set may be a CSI-SSB resource set, including a set of SSB resources (e.g., SSB index).

[0082] In some implementations, a CSI report configuration may include an indicator that indicates the configured CSI report is dedicated to AI / ML transmission.

[0083] In some implementations, a specific CSI resource configuration index may be used to configure CSI resources for Set A of beams.

[0084] Priority rule

[0085] In some implementations, a CSI report may be associated with a priority value PriiCSI. In some implementations, a first CSI report may be regarded as having priority over a second CSI report if the priority value associated with the first CSI report is lower than the priority value associated with the second CSI report. In some implementations, a second CSI report may be regarded as having priority over a first CSI report if the priority value associated with the first CSI report is lower than the priority value associated with the second CSI report.

[0086] In some implementations, when the first CSI report and the second CSI report are both periodic CSI reports to be carried on a first PUCCH and a second PUCCH, the UE may multiplex the first CSI report with the second CSI report on the PUCCH or PUSCH. If the resource for PUCCH or PUSCH, particularly the PUSCH resource on which UCI is multiplexed, is insufficient to transmit both the first CSI report and the second CSI report, the UE may apply the priority value to determine which CSI report is to be dropped.

[0087] In some implementations, when the first CSI report is an aperiodic CSI report to be carried on a first PUSCH and the second CSI report is a semi-persistent CSI report to be carried on a second PUSCH, the UE may apply the priority value to determine which CSI report is to be dropped.

[0088] In some implementations, the CSI report corresponding to a lower priority value may be kept and the CSI report corresponding to a higher priority value may be dropped.

[0089] In some implementations, the priority value may be associated with a parameter that is related to the AI / ML indicator. In some implementations, if a CSI report is configured / indicated as a report for AI / ML, the parameter may correspond to a lower value than a report for other purposes (e.g., Type I CSI, Type II CSI, Enhanced Type II CSI, Further Enhanced Type II Port Selection CSI, Enhanced Type II for CJT, Further Enhanced Type II Port Selection for CJT, Enhanced Type II for predicted PMI, Further Enhanced Type II Port Selection for predicted PMI and TDCP reporting).

[0090] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, s, and x. In some implementations, PriiCSI(y, k, c, s, x) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s + x.

[0091] The parameter y may correspond to the reporting type of the CSI report. For example, y=0 for aperiodic CSI reports to be carried on PUSCH; y=1 for semi-persistent CSI reports to be carried on PUSCH; y=2 for semi-persistent CSI reports to be carried on PUCCH; and y=3 for periodic CSI reports to be carried on PUCCH.

[0092] The parameter k may correspond to a data type indicator that indicates a content type of the CSI report. For example, k=0 for CSI reports carrying L1-RSRP or L1-SINR, and k=1 for CSI reports not carrying L1-RSRP or L1-SINR.

[0093] The parameter c may be the serving cell index. Ncellsmay correspond to the maximum number of serving cells, such as the value of the higher layer parameter maxNrofServingCells.

[0094] The parameter s may be the report configuration ID (e.g., the reportConfigID). Msmay correspond to the maximum number of CSI report configurations, such as the value of the higher layer parameter maxNrofCSI-ReportConfigurations.

[0095] The parameter x may be equal to 0 to represent a report with AI / ML related indication and equal to 1 to represent a report without AI / ML related indication.

[0096] In some implementations, when a non-AI / ML report and an AI / ML report correspond to the same reporting type (e.g., periodic, semi-persistent, aperiodic), the report configuration ID for the AI / ML report may be smaller than that for non-AI / ML report.

[0097] In some implementations, the priority value may be associated with a parameter (e.g., the parameter x) that corresponds to different values based on the reporting purpose (e.g., AI / ML report for inference results, AI / ML report for data collection, or AI / ML report for performance monitoring). In some implementations, AI / ML reports for different reporting purposes may be associated with different CSI resource sets, and the parameter x may have values selected from the set {0, 1, 2, 3}. For example, x=0 may represent a report corresponding to a CSI resource set for AI / ML inference purpose, x=1 may represent a report corresponding to a CSI resource set for non-AI / ML purpose, x=2 may represent a report corresponding to a CSI resource set for AI / ML performance monitoring purpose, and x=3 may represent a report corresponding to a CSI resource set for AI / ML data collection purpose. In some implementations, AI / ML report for the data collection purpose and AI / ML report for the performance monitoring may correspond to the same value.

[0098] In some implementations, CSI resource sets for different reporting purposes may have corresponding indications, such as a parameter with an enumerated value (e.g., ENUMERATED {inference, data collection, performance monitoring}). In some implementations, CSI resource sets for different purposes may correspond to different CSI resource set indexes.

[0099] In some implementations, the priority value may be associated with a parameter related to the AI / ML indicator, and the parameter may have a higher value for a report for AI / ML purpose than for a report for non-AI / ML purpose. In some implementations, AI / ML reports for different reporting purposes may be associated with different CSI resource sets, and the parameter x may have value 0, 1, 2, and 3. For example, x=0 may represent a report corresponding to a CSI resource set for non-AI / ML purpose, x=1 may represent a report corresponding to a CSI resource set for AI / ML inference purpose, x=2 may represent a report corresponding to a CSI resource set for AI / ML performance monitoring purpose, and x=3 may represent a report corresponding to a CSI resource set for AI / ML data collection purpose. In some implementations, AI / ML report for the data collection purpose and AI / ML report for the performance monitoring may correspond to the same value.

[0100] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter k may correspond to a data type indicator that indicates a content type of the CSI report. For example, k=0 for CSI reports carrying L1-RSRP or L1-SINR with AI / ML configuration / indicator, k=1 for CSI reports carrying L1-RSRP or L1-SINR without AI / ML configuration / indicator, and k=2 for CSI reports not carrying L1-RSRP or L1-SINR.

[0101] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter k may correspond to a data type indicator that indicates a content type of the CSI report. For example, k=0 for CSI reports carrying L1-RSRP or L1-SINR with AI / ML inference configuration / indicator, k=1 for CSI reports carrying L1-RSRP or L1-SINR without AI / ML configuration / indicator, k=2 for CSI reports carrying L1-RSRP or L1-SINR for AI / ML performance monitoring or data collection configuration / indicator, and k=3 for CSI reports not carrying L1-RSRP or L1-SINR. In some implementations, CSI reports carrying L1-RSRP or L1-SINR for AI / ML performance monitoring or data collection configuration / indicator may correspond to the same value as CSI reports not carrying L1-RSRP or L1-SINR.

[0102] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter k may correspond to a data type indicator that indicates a content type of the CSI report. For example, k=0 for CSI reports carrying L1-RSRP or L1-SINR without AI / ML configuration / indicator, k=1 for CSI reports carrying L1-RSRP or L1-SINR with AI / ML configuration / indicator, and k=2 for CSI reports not carrying L1-RSRP or L1-SINR.

[0103] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter k may correspond to a data type indicator that indicates a content type of the CSI report. For example, k=0 for CSI reports carrying L1-RSRP or L1-SINR without AI / ML inference configuration / indicator, k=1 for CSI reports carrying L1-RSRP or L1-SINR with AI / ML inference configuration / indicator, k=2 for CSI reports carrying L1-RSRP or L1-SINR for AI / ML performance monitoring or data collection configuration / indicator, and k=3 for CSI reports not carrying L1-RSRP or L1-SINR.

[0104] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter s may be the report configuration ID. In some implementations, the CSI report configuration for AI / ML purpose may correspond to the smallest CSI report configuration index.

[0105] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter s may be the report configuration ID. CSI report configurations for different AI / ML purposes may correspond to different CSI report configuration indices. In some implementations, the CSI report configuration for AI / ML inference may correspond to the smallest CSI report configuration index.

[0106] In some implementations, the priority value PriiCSImay be associated with parameters y, k, c, and s. In some implementations, the priority value PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter s may be the report configuration ID. In some implementations, the CSI report configuration for set A of beams may have a smaller ID value than that for set B of beams. The CSI report configuration ID for set A of beams may differ from the CSI report configuration ID for set B of beams.

[0107] In some implementations, CSI reports for set A of beams (e.g., predicted results) may be prioritized over CSI reports for set B of beams (e.g., measurement results). In some implementations, CSI reports for set A of beams may correspond to a first CSI resource configuration index (e.g., CSI-resource setting) and CSI reports for set B of beams may correspond to a second CSI resource configuration index. The priority value may be associated with a parameter related to the CSI resource configuration, and the first CSI resource configuration index may have a smaller value than the second CSI resource configuration index. In some implementations, CSI reports for set A of beams may correspond to a first CSI report configuration index and CSI reports for set B of beams may correspond to a second CSI report configuration index, and the first CSI report configuration index may have a smaller value than the second CSI report configuration index.

[0108] In some implementations, a CSI report for inference (e.g., including inference results) may be prioritized over either a CSI report for monitoring (e.g., including monitoring results) or a CSI report for data collection (e.g., including training results). The priority value may be further associated with a parameter F, where the value of the parameter F may be selected from {0,1,2}. Each value of the parameter F may correspond to an AI / ML function, such as inference, monitoring, or training. Different CSI reports for different functions may be prioritized accordingly. It should be noted that the values of parameter F provided above are merely examples. The parameter F may be extended to include other AI / ML functions as further specified, and the values associated with different functions may follow a different order depending on implementation.

[0109] In some implementations, a CSI report for NW-side AI / ML model may be prioritized over a CSI report for UE-side AI / ML model. The priority value may be further associated with a parameter G, where the value of the parameter G may be selected from {0,1,2}. Each value of the parameter G may correspond to a NW-side, UE-side, or two-side AI / ML model. It should be noted that the values of the parameter G provided above are merely examples. The parameter G may be extended to include other AI / ML functions as further specified, and the values associated with different functions may follow a different order depending on implementation.

[0110] In some implementations, a CSI report for an AI / ML model may be prioritized among different models, functionalities, or additional conditions. The priority value may be further associated with a parameter FGI, which includes a list of values. When more than one CSI report is configured for AI / ML operations and is triggered, the UE may determine which associated CSI report should be prioritized for transmission on the PUCCH or PUSCH.

[0111] Multiplexing rule

[0112] In some implementations, when a first CSI report with the AI / ML indicator and a second CSI report without the AI / ML indicator correspond to the same CSI report type (e.g., aperiodic CSI report on PUSCH, semi-persistent CSI report on PUSCH, semi-persistent CSI report on PUCCH, periodic CSI report on PUCCH) and overlap in time domain, and when an UL resource is available (e.g., the resource is big enough) to carry both the first CSI report and the second CSI report, the UE may multiplex the first CSI report with the second CSI report on the UL resource. Otherwise, if the UL resource is insufficient to carry both the first CSI report and the second CSI report, the UE may drop one of the first CSI report and the second CSI report based on the priority value.

[0113] In some implementations, the order of multiplexing UCI on an UL resource may be based on the AI / ML indicator. For example, the first CSI report may be placed before the second CSI report in the UL resource, and thus the second CSI report may be partially or fully dropped if the UL resource is not enough for accommodating both the first CSI report and the second CSI report.

[0114] In some implementations, the order of multiplexing UCI on an UL resource may be based on the AI / ML indicator. For example, the first CSI report may be placed after the second CSI report in the UL resource, and thus the first CSI report may be partially or fully dropped if the UL resource is not enough for accommodating both the first CSI report and the second CSI report.

[0115] In some implementations, the UE may not expect to multiplex the first CSI report with other UCI (e.g., HARQ-ACK bit, SR, LRR) on the UL resource.

[0116] In some implementations, the UE may not expect to multiplex the first CSI report with other UCI (e.g., HARQ-ACK bit, SR, LRR, a CSI report without the AI / ML indicator, such as the second CSI report) on the UL resource.

[0117] FIG. 2 is a flowchart illustrating a method / process 200 performed by a UE for performing a beam management operation, according to an example implementation of the present disclosure. In the action 202, the process 200 may start by receiving, from a BS, a first CSI report configuration for configuring a first CSI report. The first CSI report configuration may include an indicator for indicating that the first CSI report is for AI / ML purpose. In the action 204, the process 200 may receive, from the BS, a second CSI report configuration for configuring a second CSI report.

[0118] In the action 206, the process 200 may determine a first priority for the first CSI report based on at least one first parameter associated with the first RRC configuration. For example, the UE may calculate the first priority based on the at least one first parameter associated with the first RRC configuration, such as parameter y, k, c, s, x, as disclosed in the present disclosure. In some implementations, calculation of the first priority may be based on the formula PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s. In some implementations, calculation of the first priority may be based on the formula PriiCSI(y, k, c, s, x) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s + x.

[0119] In the action 208, the process 200 may determine a second priority for the second CSI report based on at least one second parameter associated with the second RRC configuration. For example, the UE may calculate the second priority based on the at least one second parameter associated with the second RRC configuration, such as parameter y, k, c, s, x, as disclosed in the present disclosure.

[0120] In the action 210, the process 200 may determine that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority. For example, the UE may determine that the second CSI report that is not used for the AI / ML purpose (e.g., for measurement results) has a higher priority than the first CSI report that is used for the AI / ML purpose (e.g., for predicted results). The process 200 may then end.

[0121] The steps / actions shown in FIG. 2 should not be construed as necessarily order dependent. The order in which the process is described is not intended to be construed as a limitation. Moreover, some of the actions shown in FIG. 2 may be omitted in some implementations and one or more actions shown in FIG. 2 may be combined.

[0122] The technical problem addressed by the method illustrated in FIG. 2 relates to determining the priority for CSI reports with different purposes. Based on the parameters associated with the corresponding CSI report configuration, the UE may determine that the second CSI report for non-AI / ML purpose has a higher priority than the first CSI report for the AI / ML purpose. Since the CSI reports have different priorities, the UE may be able to prioritize the second CSI report over the first CSI report when these CSI reports overlap in time domain.

[0123] The technical problem addressed by the method illustrated in FIG. 2 relates to determining the priority for CSI reports with different purposes, such as standard operation and AI / ML-related processing. As such, the UE may efficiently handle overlapping CSI reports configured for different purposes. In particular, the method enables the UE to determine respective priorities for each CSI report based on parameters associated with their configurations. For example, the UE may determine that the second CSI report (configured for non-AI / ML purposes) has a higher priority than the first CSI report (configured for AI / ML purposes). As a result, when the CSI reports overlap in the time domain, the UE can prioritize transmission of the higher-priority report. This improves transmission efficiency and ensures that critical CSI information for core operations is not delayed or dropped.

[0124] In some implementations, the first priority may correspond to a first priority value (e.g., the PriiCSIin the formula disclosed above), and the second priority may correspond to a second priority value. In some implementations, the first priority value may be greater than the second priority value. In other words, a lower priority value may correspond to a higher priority. In some implementations, the first priority value may be less than the second priority value. In other words, a lower priority value may correspond to a lower priority.

[0125] In some implementations, the UE may transmit, to the BS, the second CSI report and drop the first CSI report in response to determining that the first CSI report and the second CSI report overlap in time domain. For example, the CSI report corresponding to a lower priority value may be kept and the CSI report corresponding to a higher priority value may be dropped. When two CSI reports overlap in time domain, the UE may transmit only the CSI report having a lower priority value and drop the CSI report having a higher priority value.

[0126] In some implementations, the UE may transmit, to the BS, the second CSI report and a portion of the first CSI report on a PUSCH in response to determining that the first CSI report and the second CSI report overlap in time domain. For example, the UE may determine how to perform UCI multiplexing (e.g., on a PUSCH) based on an order related to the AI / ML indicator. In some implementations, when allocating a UL resource (e.g., a PUSCH) used for UCI multiplexing, the first CSI report that is used for the AI / ML purpose (e.g., for predicted results) may be placed after the second CSI report that is not used for the AI / ML purpose (e.g., for measurement results). When the UL resource is sufficient to accommodate the first CSI report and the second CSI report completely, the UE may partially or fully drop the first CSI report.

[0127] In some implementations, the at least one first parameter may include a first report configuration ID included in the first CSI report configuration, and the at least one second parameter may include a second report configuration ID included in the second CSI report configuration. For example, calculation of the first / second priority may be based on the formula PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter s is the first / second report configuration ID included in the first / second CSI report configuration. In some implementations, a CSI report configuration for the AI / ML purpose may correspond to the smallest CSI report configuration ID. In some implementations, a CSI report configuration for the AI / ML purpose may correspond to the largest CSI report configuration ID.

[0128] In some implementations, the at least one first parameter may include a first data type indicator that indicates a first content type of the first CSI report, and the at least one second parameter may include a second data type indicator that indicates a second content type of the second CSI report. For example, calculation of the first / second priority may be based on the formula PriiCSI(y, k, c, s) = 2・Ncells・Ms・y + Ncells・Ms・k + Ms・c + s, where the parameter k may be the first / second data type indicator that indicates the first / second content type of the first / second CSI report.

[0129] In some implementations, the first CSI report may include a first beam report of predicted results, which may be used for the AI / ML purpose. The second CSI report may include a second beam report of measurement results.

[0130] FIG. 3 is a flowchart illustrating a method / process 300 performed by a BS for configuring a beam management operation, according to an example implementation of the present disclosure. In the action 302, the process 300 may start by transmitting, to a UE, a first CSI report configuration for configuring a first CSI report. The first CSI report configuration may include an indicator for indicating that the first CSI report is for AI / ML purpose. In the action 304, the process 300 may transmit, to the UE, a second CSI report configuration for configuring a second CSI report.

[0131] A first priority for the first CSI report may be based on at least one first parameter associated with the first RRC configuration. A second priority for the second CSI report may be based on at least one second parameter associated with the second RRC configuration. The UE may determine that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority. The process 300 may then end. The method illustrated in FIG. 3 is similar to that in FIG. 2, except that it is described from the perspective of the BS (instead of the UE).

[0132] FIG. 4 is a block diagram illustrating a node 400 for wireless communication in accordance with various aspects of the present disclosure. As illustrated in FIG. 4, a node 400 may include a transceiver 420, a processor 428, a memory 434, one or more presentation components 438, and at least one antenna 436. The node 400 may also include a radio frequency (RF) spectrum band module, a BS communications module, a network communications module, and a system communications management module, Input / Output (I / O) ports, I / O components, and a power supply (not illustrated in FIG. 4).

[0133] Each of the components may directly or indirectly communicate with each other over one or more buses 440. The node 400 may be a UE or a BS that performs various functions disclosed with reference to FIGS. 1 through 3.

[0134] The transceiver 420 has a transmitter 422 (e.g., transmitting / transmission circuitry) and a receiver 424 (e.g., receiving / reception circuitry) and may be configured to transmit and / or receive time and / or frequency resource partitioning information. The transceiver 420 may be configured to transmit in different types of subframes and slots including, but not limited to, usable, non-usable, and flexibly usable subframes and slot formats. The transceiver 420 may be configured to receive data and control channels.

[0135] The node 400 may include a variety of computer-readable media. Computer-readable media may be any available media that may be accessed by the node 400 and include volatile (and / or non-volatile) media and removable (and / or non-removable) media.

[0136] The computer-readable media may include computer-storage media and communication media. Computer-storage media may include both volatile (and / or non-volatile media), and removable (and / or non-removable) media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or data.

[0137] Computer-storage media may include RAM, ROM, EPROM, EEPROM, flash memory (or other memory technology), CD-ROM, Digital Versatile Disks (DVD) (or other optical disk storage), magnetic cassettes, magnetic tape, magnetic disk storage (or other magnetic storage devices), etc. Computer-storage media may not include a propagated data signal. Communication media may typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transport mechanisms and include any information delivery media.

[0138] The term “modulated data signal” may mean a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. Communication media may include wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above listed components should also be included within the scope of computer-readable media.

[0139] The memory 434 may include computer-storage media in the form of volatile and / or non-volatile memory. The memory 434 may be removable, non-removable, or a combination thereof. Example memory may include solid-state memory, hard drives, optical-disc drives, etc. As illustrated in FIG. 4, the memory 434 may store a computer-readable and / or computer-executable instructions 432 (e.g., software codes) that are configured to, when executed, cause the processor 428 to perform various functions disclosed herein, for example, with reference to FIGS. 1 through 3. Alternatively, the instructions 432 may not be directly executable by the processor 428 but may be configured to cause the node 400 (e.g., when compiled and executed) to perform various functions disclosed herein.

[0140] The processor 428 (e.g., having processing circuitry) may include an intelligent hardware device, e.g., a Central Processing Unit (CPU), a microcontroller, an ASIC, etc. The processor 428 may include memory. The processor 428 may process the data 430 and the instructions 432 received from the memory 434, and information transmitted and received via the transceiver 420, the baseband communications module, and / or the network communications module. The processor 428 may also process information to send to the transceiver 420 for transmission via the antenna 436 to the network communications module for transmission to a CN.

[0141] One or more presentation components 438 may present data indications to a person or another device. Examples of presentation components 438 may include a display device, a speaker, a printing component, a vibrating component, etc.

[0142] In view of the present disclosure, it is obvious that various techniques may be used for implementing the disclosed concepts without departing from the scope of those concepts. Moreover, while the concepts have been disclosed with specific reference to certain implementations, a person of ordinary skill in the art may recognize that changes may be made in form and detail without departing from the scope of those concepts. As such, the disclosed implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present disclosure is not limited to the particular implementations disclosed and many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.

Claims

1. A User Equipment (UE) for performing a beam management operation, the UE comprising: 　　　　at least one processor; and 　　　　at least one non-transitory computer-readable medium coupled to the at least one processor and storing one or more computer-executable instructions that, when executed by the at least one processor, cause the UE to: 　　　　　　　　receive, from a base station (BS), a first Channel State Information (CSI) report configuration for configuring a first CSI report, the first CSI report configuration comprising an indicator for indicating that the first CSI report is for Artificial Intelligence (AI) / Machine Learning (ML) purpose; 　　　　　　　　receive, from the BS, a second CSI report configuration for configuring a second CSI report; 　　　　　　　　determine a first priority for the first CSI report based on at least one first parameter associated with the first RRC configuration; 　　　　　　　　determine a second priority for the second CSI report based on at least one second parameter associated with the second RRC configuration; and 　　　　　　　　determine that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

2. The UE of claim 1, wherein: 　　　　the first priority corresponds to a first priority value, 　　　　the second priority corresponds to a second priority value, and 　　　　the first priority value is greater than the second priority value.

3. The UE of claim 1, wherein the one or more computer-executable instructions, when executed by the at least one processor, further cause the UE to: 　　　　transmit, to the BS, the second CSI report and drop the first CSI report in response to determining that the first CSI report and the second CSI report overlap in time domain.

4. The UE of claim 1, wherein the one or more computer-executable instructions, when executed by the at least one processor, further cause the UE to: 　　　　transmit, to the BS, the second CSI report and a portion of the first CSI report on a Physical Uplink Shared Channel (PUSCH) in response to determining that the first CSI report and the second CSI report overlap in time domain.

5. The UE of claim 1, wherein: 　　　　the at least one first parameter comprises a first report configuration identifier (ID) included in the first CSI report configuration, and 　　　　the at least one second parameter comprises a second report configuration ID included in the second CSI report configuration.

6. The UE of claim 1, wherein: 　　　　the at least one first parameter comprises a first data type indicator that indicates a first content type of the first CSI report, and 　　　　the at least one second parameter comprises a second data type indicator that indicates a second content type of the second CSI report.

7. The UE of claim 1, wherein: 　　　　the first CSI report comprises a first beam report of predicted results, and 　　　　the second CSI report comprises a second beam report of measurement results.

8. A Base Station (BS) for configuring a beam management operation, the BS comprising: 　　　　at least one processor; and 　　　　at least one non-transitory computer-readable medium coupled to the at least one processor and storing one or more computer-executable instructions that, when executed by the at least one processor, cause the BS to: 　　　　　　　　transmit, to a User Equipment (UE), a first Channel State Information (CSI) report configuration for configuring a first CSI report, the first CSI report configuration comprising an indicator for indicating that the first CSI report is for Artificial Intelligence (AI) / Machine Learning (ML) purpose; and 　　　　　　　　transmit, to the UE, a second CSI report configuration for configuring a second CSI report, wherein: 　　　　　　　　a first priority for the first CSI report is based on at least one first parameter associated with the first RRC configuration, 　　　　　　　　a second priority for the second CSI report is based on at least one second parameter associated with the second RRC configuration, and 　　　　　　　　the UE determines that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

9. The BS of claim 8, wherein: 　　　　the first priority corresponds to a first priority value, 　　　　the second priority corresponds to a second priority value, and 　　　　the first priority value is greater than the second priority value.

10. The BS of claim 8, wherein the one or more computer-executable instructions, when executed by the at least one processor, further cause the BS to: 　　　　receive, from the UE, the second CSI report, wherein: 　　　　the UE determines to transmit, to the BS, the second CSI report and drop the first CSI report in response to determining that the first CSI report and the second CSI report overlap in time domain.

11. The BS of claim 8, wherein the one or more computer-executable instructions, when executed by the at least one processor, further cause the BS to: 　　　　receive, from the UE, the second CSI report and a portion of the first CSI report on a Physical Uplink Shared Channel (PUSCH) in a case that the first CSI report and the second CSI report overlap in time domain.

12. The BS of claim 8, wherein: 　　　　the at least one first parameter comprises a first report configuration identifier (ID) included in the first CSI report configuration, and 　　　　the at least one second parameter comprises a second report configuration ID included in the second CSI report configuration.

13. The BS of claim 8, wherein: 　　　　the at least one first parameter comprises a first data type indicator that indicates a first content type of the first CSI report, and 　　　　the at least one second parameter comprises a second data type indicator that indicates a second content type of the second CSI report.

14. The BS of claim 8, wherein: 　　　　the first CSI report comprises a first beam report of predicted results, and 　　　　the second CSI report comprises a second beam report of measurement results.

15. A method performed by a User Equipment (UE) for performing a beam management operation, the method comprising: 　　　　receiving, from a base station (BS), a first Channel State Information (CSI) report configuration for configuring a first CSI report, the first CSI report configuration comprising an indicator for indicating that the first CSI report is for Artificial Intelligence (AI) / Machine Learning (ML) purpose; 　　　　receiving, from the BS, a second CSI report configuration for configuring a second CSI report; 　　　　determining a first priority for the first CSI report based on at least one first parameter associated with the first RRC configuration; 　　　　determining a second priority for the second CSI report based on at least one second parameter associated with the second RRC configuration; and 　　　　determining that the second CSI report has a higher priority than the first CSI report based on the first priority and the second priority.

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