Abstract: Various embodiments herein provide techniques related to communication between a non real-time (Non-RT) radio access network (RAN) intelligent controller (RIC) and a near real-time (Near-RT) RIC. Specifically, the Non-RT RIC may identify physical positioning information related to a user equipment (UE); generate enrichment information (EI) related to the physical positioning information; and transmit the EI over an A1 interface to the Near-RT RIC. Other embodiments may be described and/or claimed.

Abstract: Devices and methods for performing a beam management operation are provided in this disclosure. A radio communication device may include a transceiver that is configured to transmit a plurality of reference signals, each reference signal being beamformed based on a set of beamforming weights that is different from a set of beamforming weights of at least one other reference signal, and receive a plurality of measurement results representing measurements for at least some of the plurality of reference signals. The radio communication device may further include a processor that is configured to provide the plurality of measurement results to a machine learning model configured to determine a parameter for a beam management operation using a predefined codebook and perform the beam management operation according to the predefined codebook based on the determined parameter.

Abstract: Logic may determine user equipment (UE) context information associated with a UE, the UE context information comprising new information related to establishment of a new connection with the UE or related to an update of the UE context information for an existing connection with the UE. Logic may identify an event trigger associated with new information. And logic may respond to a query from the near-RT RIC for UE context information associated with the UE, wherein the UE supports a non-grid of beams mode for beamforming.

Abstract: According to various examples, a receiver is described, comprising a memory and a processor, the processor being configured to receive, for each of a plurality of receive antennas, a pilot signal via the receive antenna, wherein the pilot signal has a component for each of a plurality of subcarriers, generate, for each of the plurality of receive antennas, an intermediate power delay profile estimation processing result by processing the pilot signal received via the receive antenna by one or more first neural network layers combining the components of the pilot signal received via the receive antenna for the sub-carriers, generate a combined intermediate power delay profile estimation processing result by combining the intermediate power delay profile estimation processing results generated for the receive antennas and determine a power delay profile estimate by processing the combined intermediate power delay profile processing estimation result by one or more second neural network layers.

Abstract: An apparatus for use in a RAN node includes processing circuitry. To configure the RAN node for signal estimation in an O-RAN network, the processing circuitry detects, at a distributed unit (DU) function of the RAN node, a compressed sounding reference signal (SRS). The compressed SRS is based on a received SRS. Partial decompression of the compressed SRS is performed to generate a partially decompressed signal. The partial decompression is based at least on a weight matrix associated with the compressed SRS. A plurality of power delay profile (PDP) signals is generated based on the partially decompressed signal. Averaging of the plurality of PDP signals is performed to generate an output PDP signal corresponding to the received SRS.

Abstract: According to various examples, a communication system is described comprising a receiver configured to receive, for each of a plurality of object classes, via a wireless communication channel shared among transmitters of a respective set of transmitters, a superposition of transmitted hyperdimensional code words, comprising, for each transmitter of the respective set of transmitters, a hyperdimensional code word transmitted via the wireless communication channel and encoding data of an object of the object class acquired by the transmitter, a memory configured to store, for each of the plurality of object classes, the received superposition in association with the class, a processor configured to classify a hyperdimensional code word representing an object to be classified by correlating the hyperdimensional code word with each stored superposition and to generate a classification result corresponding to the object class associated with a superposition fulfilling a predetermined criterion based on correlation

Abstract: A disclosed example includes identifying an event in a radio access network (RAN) based on RAN metrics data; accessing a machine learning (ML) model of the RAN; generating predictions of the ML model of the RAN based on a calibration dataset; determining uncertainty scores corresponding to the predictions of the ML model of the RAN; determining a conformity threshold based on the uncertainty scores; and updating a robustness protection layer in an inference pipeline to include the conformity threshold.

Abstract: Equipment for controlling a node in a wireless communication system can include a processor. The processor can identify scenarios under which at least one wireless communication system problem can occur, and metrics that characterize the scenarios. Based on the metrics the processor can generate a set of sub-problems, where a sub-problem of the set can correspond to a respective scenario. The processor can identify a set of algorithms for responding to respective sub-problems of the set of sub-problems to generate a module. The processor can provide control logic to determine which algorithm of the module to execute during operation of the wireless communication system based on detection of problems of an identified scenario. The processor can provide identification of a selected algorithm based on the determining. Communication circuitry can provide identification of the selected algorithm for execution within the wireless communication system.

Abstract: A radio communication device may include a memory; and a processor configured to: perform a plurality of channel estimations based on a received radio signal comprising a plurality of reference signals of a plurality of mobile radio communication devices, wherein each channel estimation of the plurality of channel estimations is for a respective mobile radio communication device of the plurality of communication devices; determine a residual signal for the plurality of mobile radio communication devices based on the plurality of channel estimations; and estimate channel information for at least one mobile radio communication device from the plurality of radio communication device based on the residual signal and an estimated power delay profile.

Abstract: Techniques are disclosed to address issues related to the computation of channel state information (CSI) and angular spectrum (AS) to perform beamforming. The CSI and AS, as well as various statistical channel parameters of a wireless channel, may be computed using different techniques, which include the use of domain knowledge enhanced neural networks (DKE-NNs). The CSI and AS may be further utilized to perform beamforming using various techniques. One of these techniques may include the implementation of eigen beamforming, which provides artificially generated power at locations within the AS that are identified with estimated eigenvector beam locations. As a result of the artificially-generated power, the resulting vector decomposition used to provide the beamforming weights results in widened eigenvector beams.

Abstract: Various embodiments herein provide techniques for reducing computational complexity in multiple input multiple output (MIMO, e.g., massive MIMO (mMIMO)) communications, such as efficient decomposition of a channel covariance matrix. Embodiments enable accurate mMIMO channel estimation with relatively low computational complexity compared with prior techniques. Additionally, embodiments may provide efficient beamforming and/or spatial compression. Other embodiments may be described and claimed.

Abstract: Devices and methods for performing a beam management operation are provided in this disclosure. A radio communication device may include a transceiver that is configured to transmit a plurality of reference signals, each reference signal being beamformed based on a set of beamforming weights that is different from a set of beamforming weights of at least one other reference signal, and receive a plurality of measurement results representing measurements for at least some of the plurality of reference signals. The radio communication device may further include a processor that is configured to provide the plurality of measurement results to a machine learning model configured to determine a parameter for a beam management operation using a predefined codebook and perform the beam management operation according to the predefined codebook based on the determined parameter.

Abstract: According to various examples, communication device is described comprising a receiver configured to receive a signal from another communication device via a radio channel and a processor configured to estimate a power delay profile of the radio channel by maximum likelihood estimation of the power delay profile from the received signal and perform receive signal processing in accordance with the estimated power delay profile.

Abstract: Various embodiments herein are directed to beamforming associated with multiple-input multiple-output (MIMO) modes in open radio access network (O-RAN) systems. In one embodiment, an apparatus comprises: memory to store beamforming configuration information associated with a plurality MIMO modes; and processing circuitry, coupled with the memory to: retrieve the beamforming configuration information from the memory; request, based on the beamforming configuration information, measurements associated with the plurality of MIMO modes; receive the measurements associated with the plurality of MIMO modes; and based on the received measurements, train an artificial intelligence/machine learning (AI/ML) model that is to predict relative beamforming performance between the plurality of MIMO modes.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Abstract: Systems and methods of beamforming and improving mmWave communications for drones are described. Multiple RF chains are used to adapt the main beam to track changes without the use of pilot signals. To reduce interference, interfering signal power is eliminated by optimizing a non-Gaussian measure to extract the interferers. The AoA of signals from a target drone on neighbouring drones and location of the neighbouring drones and base stations are used to independently corroborate the location reported by the target drone. The base station provides additional synchronization signals below 6 GHz and restricts the search/measurement space in the vertical direction. The inherent sparse structure above 6 GHz is exploited by applying different beamformers on a sounding signal and estimating the AoA and impulse response. Variations of fully digital and hybrid beamforming architectures for multi-cell DL sync and CRS measurement are described.

Abstract: Various embodiments herein provide techniques related to communication between a non real-time (Non-RT) radio access network (RAN) intelligent controller (RIC) and a near real-time (Near-RT) RIC. Specifically, the Non-RT RIC may identify physical positioning information related to a user equipment (UE); generate enrichment information (EI) related to the physical positioning information; and transmit the EI over an A1 interface to the Near-RT RIC. Other embodiments may be described and/or claimed.

Abstract: According to various examples, a communication system is described comprising a receiver configured to receive, for each of a plurality of object classes, via a wireless communication channel shared among transmitters of a respective set of transmitters, a superposition of transmitted hyperdimensional code words, comprising, for each transmitter of the respective set of transmitters, a hyperdimensional code word transmitted via the wireless communication channel and encoding data of an object of the object class acquired by the transmitter, a memory configured to store, for each of the plurality of object classes, the received superposition in association with the class, a processor configured to classify a hyperdimensional code word representing an object to be classified by correlating the hyperdimensional code word with each stored superposition and to generate a classification result corresponding to the object class associated with a superposition fulfilling a predetermined criterion based on correlation

Abstract: Various embodiments herein provide techniques for reducing computational complexity in multiple input multiple output (MIMO, e.g., massive MIMO (mMIMO)) communications, such as efficient decomposition of a channel covariance matrix. Embodiments enable accurate mMIMO channel estimation with relatively low computational complexity compared with prior techniques. Additionally, embodiments may provide efficient beamforming and/or spatial compression. Other embodiments may be described and claimed.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.