Abstract: A method includes receiving a pilot signal or a measurement report from a user equipment (UE) or a base station (BS). The method also includes updating a CSI buffer with channel state information (CSI) obtained from the pilot signal or the measurement report, the CSI buffer configured to store previous uplink or downlink channel estimates. The method also includes providing at least a portion of the CSI buffer to a CSI predictor comprising an artificial intelligence (AI) model that utilizes one or more weight sharing mechanisms, the AI model comprising a sequence of layers. The method also includes predicting temporal CSI using the CSI predictor. Depending on the configured output, the method can also include and/or be used for denoising and frequency extrapolation.

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: 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 identifying network anomalies. A method includes determining a cumulative anomaly score over a predefined time range based on a subset of historical PM samples and determining an anomaly ratio of a first time window and a second time window, based on the cumulative anomaly score. The method also includes determining one or more anomaly events coinciding with CM parameter changes based on the anomaly ratio; collating the PM, alarm, and CM data into a combined data set based on matching fields and timestamps; generating a set of rules linking one or more CM parameter changes and the collated data to anomaly events; and generating root cause explanations for CM parameter changes that are linked to anomaly events.

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 communication and bistatic object sensing involves transmitting, to a UE, a bistatic object sensing configuration. The bistatic object sensing configuration configures the UE to one of receive a sensing signal transmitted by a base station or transmit the sensing signal for reception by the base station. The bistatic object sensing configuration indicates sensing transmission power, waveform, and sensing resources and periodicity for the sensing signal, and may configure the UE to one of receive or transmit an object detection 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: 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: Sparsity assisted channel state information (CSI) reporting or two-step CSI reporting is enabled or disabled for a user equipment. The configuration enabling/disabling sparsity assisted truncation for CSI reporting or two-step CSI reporting includes a domain for transformation of CSI for the sparsity assisted CSI reporting, and a time window for correlated channel. When the configuration enables sparsity assisted truncation for CSI reporting, a fixed number of non-zero coefficients and one or more threshold values for delay/angle or doppler for truncation are configured. When sparsity assisted truncation for CSI reporting is enabled, received the CSI reference signals are measured based on the configuration and a CSI report is transmitted indicating a specific range for which truncation occurred. When two-step CSI reporting is enabled, CSI compression corresponding to the at least one configuration and the time window is performed and CSI feedback is transmitted along with a flag.

Abstract: A method for discovering and diagnosing network anomalies. The method includes receiving key performance indicator (KPI) data and alarm data. The method includes extracting features based on samples obtained by discretizing the KPI data and the alarm data. The method includes generating a set of rules based on the features. The method includes identifying a sample as a normal sample or an anomaly sample. In response to identifying the sample as the anomaly sample, the method includes identifying a first rule that corresponds to the sample, wherein the first rule indicates symptoms and root causes of an anomaly included in the sample. The method further includes applying the root causes to derive a root cause explanation of the anomaly and performing a corrective action to resolve the anomaly based on the first rule.

Abstract: Machine learning (ML) assisted channel state information (CSI) reporting or ML assisted CSI prediction includes receiving CSI reporting configurations that include indications that enable or disable at least one of: ML-assisted CSI prediction and artificial intelligence channel feature information (AI-CFI) reporting. ML model training is performed or trained ML model parameters are received, and CSI reference signals corresponding to at least one of the CSI reporting configurations are received. If ML-assisted CSI prediction is enabled, the CSI reporting configurations further include: a timing offset for future CSI prediction, and ML configurations including indication of an ML model used for the ML-assisted CSI prediction. If AI-CFI reporting is enabled, the CSI reporting configurations further include: a configuration for a report of the AI-CFI, and ML configurations including indication of an ML model used for the ML assisted-CSI feedback determination.

Abstract: Configuration information for a machine learning handover event may be used by an artificial intelligence/machine learning agent configured to determine whether to initiate handover. The determination of whether to initiate handover according to the received configuration information for the machine learning handover event is based on one or more of: signal quality for one or more serving base stations, signal quality for one or more neighboring base stations, a velocity of the UE, a location of the UE, and a trajectory of the UE.

Abstract: Apparatuses and methods for identifying network anomalies. A method includes determining a cumulative anomaly score over a predefined time range based on a subset of historical PM samples and determining an anomaly ratio of a first time window and a second time window, based on the cumulative anomaly score. The method also includes determining one or more anomaly events coinciding with CM parameter changes based on the anomaly ratio; collating the PM, alarm, and CM data into a combined data set based on matching fields and timestamps; generating a set of rules linking one or more CM parameter changes and the collated data to anomaly events; and generating root cause explanations for CM parameter changes that are linked to anomaly events.

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: A method for discovering and diagnosing network anomalies. The method includes receiving key performance indicator (KPI) data and alarm data. The method includes extracting features based on samples obtained by discretizing the KPI data and the alarm data. The method includes generating a set of rules based on the features. The method includes identifying a sample as a normal sample or an anomaly sample. In response to identifying the sample as the anomaly sample, the method includes identifying a first rule that corresponds to the sample, wherein the first rule indicates symptoms and root causes of an anomaly included in the sample. The method further includes applying the root causes to derive a root cause explanation of the anomaly and performing a corrective action to resolve the anomaly based on the first rule.