Abstract: Machine learning (ML) adaptation of any one of reference signal (RS) temporal density, RS frequency density, RS spatial density, or number of transmission/reception points (TRPs) that transmit RS provides configuration of lower RS densities or fewer TRPs that transmit RS without significant loss of throughput in appropriate circumstances. Determinations to switch from high density transmission to low density transmission, to reduce the number of antenna ports or TRPs that transmit RS, or to fallback to high density transmission may be made by the ML model, optionally with UE assistance information.

Abstract: Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.

Abstract: Methods and devices for transmission symbol modulation that accounts for distortion introduced by a power amplifier (PA). A user equipment (UE) comprises a transceiver and a processor. The transceiver is configured to transmit, to a base station (BS), UE capability information that indicates support for adjusted symbol constellations, wherein the adjusted symbol constellations are adjusted to pre-compensate for distortion effects of a power amplifier (PA) in the transceiver, and receive, from the BS, a modulation and coding scheme (MCS) indication. The processor is configured to determine, based on the MCS indication, an adjusted symbol constellation, and generate modulation symbols from input bits according to the determined adjusted symbol constellation. The transceiver is further configured to transmit, to the BS, the modulation symbols.

Abstract: Methods and apparatuses for dynamically adjusting parameters of a modulation scheme and parameters of a digital pre-distorter (DPD) to pre-compensate for distortion effects of a power amplifier (PA). A user equipment (UE) comprises a modulator configured to generate modulation symbols from input bits according to an adjusted symbol constellation, a DPD operably configured to generate pre-distorted symbols, and a PA configured to amplify the pre-distorted symbols to generate transmission symbols that include distortion effects of the PA. The adjusted symbol constellation is adjusted from a target symbol constellation such that the generated modulation symbols pre-compensate for the distortion effects.

Abstract: Apparatuses and methods for support of Toeplitz-based channel state information (CSI) feedback/reporting methods. A method performed by a user equipment (UE) includes transmitting capability information indicating a capability of the UE to support a Toeplitz-based method of determining channel state information (CSI) reports; receiving configuration information that indicates parameters for the Toeplitz-based method of determining the CSI reports; and receiving CSI reference signals (RSs). The method further includes measuring the CSI-RSs; determining, based on the configuration information and the measured CSI-RSs, a CSI report; and transmitting the CSI report.

Abstract: An AI/ML monitoring operation is based on a received monitoring configuration forming part of a configuration for using an AI/ML model for a communications system operation. Based on the monitoring configuration, AI/ML model assistance information is reported, including AI/ML model monitoring results from the AI/ML monitoring operation. AI/ML model management and adaptation information based on those AI/ML model monitoring results is received, an AI/ML model management and adaptation operation is performed. The AI/ML model management and adaptation information may include parameters that characterize an action of AI/ML model management and adaptation or an indication of an action of AI/ML model management and adaptation. The action of AI/ML model management and adaptation may comprise one of model switch, model refinement or update, or model transfer.

Abstract: Methods and apparatuses for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback. A method performed by a user equipment (UE) includes transmitting capability information indicating a capability of the UE to support a ML based parameter configuration associated with CSI reports; receiving configuration information that indicates parameters for the ML based parameter configuration associated with the CSI reports; and receiving CSI reference signals (RSs). The method further includes measuring the CSI-RSs; determining, based on the configuration information, the measured CSI-RSs, and information related to localized observations of channel statistics in one or more domains, configuration parameters in the one or more domains for the CSI reports; determining, based on the configuration parameters in the one or more domains, a CSI report; and transmitting the CSI report.

Abstract: Methods and apparatuses for uplink timing and frequency synchronization in a wireless communication system. A method for operating a user equipment (UE) includes receiving, from a base station (BS), information indicating satellite ephemeris information of a communication satellite associated with the BS, a common timing advance (TA), and a compensated frequency offset (FO). The method further includes transmitting a physical random access channel (PRACH) based on the common TA and the compensated FO and receiving a random access response (RAR) indicating a UE-specific TA and FO. The method further includes, for transmission of an uplink (UL) channel, adjusting a TA and pre-compensating a FO based on the UE-specific TA and FO, respectively.

Abstract: A service management and orchestration (SMO) entity enabling a functional split between a non-real-time (RT) radio access network (RAN) intelligent controller (RIC) and an external artificial intelligence (AI)/machine learning (ML) server will, during a data collection phase, utilize the SMO entity and the non-RT RIC to collect and process RAN data and non-RAN data and, during a data transfer phase, transfer processed RAN and non-RAN data from the SMO entity to an external AI/ML server via an interface. During a training model input phase, the SMO entity receives a trained AI/ML model, metadata, and training results from the external AI/ML server via an interface and, during a configuration phase, the SMO entity uses the trained AI/ML model within the SMO entity and the non-RT RIC to transfer configuration parameters to a near-RT RIC.

Abstract: Directional sensing capability, including number of beams, beam-width, analog or digital beam sweeping, and supported waveforms, may be indicated by a user equipment (UE), which may then receive a configuration for directional sensing including number of beams, maximum or minimum beam-width, reception time between transmission beam sweeping, and waveform. The UE then performs directional sensing based on the received configuration. The configuration for directional sensing may be for one of monostatic sensing with reception periods between consecutive sensing signal transmissions or bistatic sensing using a plurality of beams. The UE's directional sensing capability may include capability of spatial multiplexing of sensing signals with communication signals. The configuration for directional sensing may permit spatial multiplexing of sensing signals with communication signals.

Abstract: A time domain resource configuration indicates a time domain resource for sensing operations by a user equipment, and a frequency domain resource configuration indicates a bandwidth part (BWP) for the sensing operations. The user equipment performs the sensing operations using the indicated time domain resource and the indicated bandwidth part. The time domain resource configuration may include a sensing type indicator S for the time domain resource for sensing operations, and may indicate that dynamic triggering of sensing is allowed. The BWP for the sensing operations may comprise BWP(s) selectively activated for the sensing operations, and may indicate BWP(s) that overlap a BWP used for cellular communication. Assistance information for interference between sensing operations and cellular communication may be transmitted by the user equipment, which may subsequently receive a configuration for coexistence of the sensing operations and the cellular communication.

Abstract: Apparatuses and methods for a CSI report configuration for CSI predictions in one or more domains. A method performed by a user equipment (UE) includes transmitting capability information indicating capability of the UE to support machine learning (ML) based channel state information (CSI) prediction in one or more domains, receiving configuration information that indicates parameters for ML based CSI prediction in the one or more domains; receiving CSI reference signals (RSs), and measuring the CSI-RSs. The method further includes determining, based on the configuration information and the measured CSI-RSs, a plurality of CSI predictions in the one or more domains; determining a CSI report including one or more of the plurality of CSI predictions and dependency information indicating dependencies between CSI predictions in the plurality of CSI predictions; and transmitting the CSI report.

Abstract: Joint configuration of cellular communications and radar sensing involves reporting of UE sensing capability information including coexistence of sensing function with cellular communication function within the UE, UE sensing hardware capability, and capability related to UE sensing parameters or modes. A sensing configuration request by the UE includes sensing application type, sensing range, and sensing periodicity. A sensing configuration by the network includes sensing transmission power, power control parameters, waveform, and sensing resources and periodicity. A sensing procedure is performed based on the sensing configuration.

Abstract: Machine learning (ML) adaptation of any one of reference signal (RS) temporal density, RS frequency density, RS spatial density, or number of transmission/reception points (TRPs) that transmit RS provides configuration of lower RS densities or fewer TRPs that transmit RS without significant loss of throughput in appropriate circumstances. Determinations to switch from high density transmission to low density transmission, to reduce the number of antenna ports or TRPs that transmit RS, or to fallback to high density transmission may be made by the ML model, optionally with UE assistance information.

Abstract: UE capability for support of machine-learning (ML) based channel environment classification may be reported by a user equipment to a base station, where the channel environment classification classifies a channel environment of a channel between the UE and a base station based on one or more of UE speed or Doppler spread, UE trajectory, frequency selectivity or delay spread, coherence bandwidth, coherence time, radio resource management (RRM) metrics, block error rate, throughput, or UE acceleration. The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification. When ML based channel environment classification is enabled, UE assistance information for ML based channel environment classification, and/or an indication of the channel environment (which may be a pre-defined channel environment associated with a lookup table), may be transmitted by the user equipment to the base station.

Abstract: Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.

Abstract: A service management and orchestration (SMO) entity enabling a functional split between a non-real-time (RT) radio access network (RAN) intelligent controller (RIC) and an external artificial intelligence (AI)/machine learning (ML) server will, during a data collection phase, utilize the SMO entity and the non-RT RIC to collect and process RAN data and non-RAN data and, during a data transfer phase, transfer processed RAN and non-RAN data from the SMO entity to an external AI/ML server via an interface. During a training model input phase, the SMO entity receives a trained AI/ML model, metadata, and training results from the external AI/ML server via an interface and, during a configuration phase, the SMO entity uses the trained AI/ML model within the SMO entity and the non-RT RIC to transfer configuration parameters to a near-RT RIC.