Abstract: A method of wireless communication by a base station includes receiving a user equipment (UE) radio capability and a UE machine learning capability. The method also includes determining a neural network function (NNF) based on the UE radio capability. The method includes determining a neural network model. The neural network model includes a model structure and a parameter set, based on the NNF, the UE machine learning capability, and a capability of a network entity. The method also includes configuring the network entity with the neural network model.

Abstract: Aspects presented herein may enable a network entity to configure a group of UEs to simultaneously transmit reference signals and to simultaneously transmit gradient vectors to the network entity, such that the network entity may receive the gradient vectors from the group of UEs as an aggregated gradient vector over the air. In one aspect, a base transmits, to a group of UEs, a configuration that configures the group of UEs to simultaneously transmit one or more group-common reference signals and to simultaneously transmit one or more gradient vectors associated with a federated learning procedure. The network entity receives, from the group of UEs, the one or more group-common reference signals and the one or more gradient vectors based on the configuration via multiple channels. The network entity calculates an average gradient vector based on the one or more group-common reference signals and the one or more gradient vectors.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a base station, a federated learning configuration that configures a participation indication to be used by the UE to indicate a participation status of the UE associated with at least one federated learning round corresponding to a machine learning component. The UE may transmit the participation indication to the base station based at least in part on the federated learning configuration. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate with a network entity within a wireless communications network. The UE may transmit a request for information to the network entity and, in response to the request, the UE may receive the requested information from the network entity. For example, the UE may request data from one or more data repositories associated with the network entity. In some examples, the information request may be associated with one or more measurements associated with operations of the network. In some instances, the UE may use a machine learning model to perform training or inference based on the information associated with the one or more measurements.

Abstract: Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.

Abstract: Various embodiments of the present technology provide a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of moderate to low bit quantization (e.g., one-bit quantization) in the receiver. Some embodiments of the error correction code minimize the probability of bit error can be obtained by perfectly training a special autoencoder, in which “perfectly” refers to finding the global minima of its cost function. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder with a suboptimum training, some embodiments utilize turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for determining neural network functions (NNFs) and configuring and using corresponding machine learning (ML) models for performing one or more ML-based wireless communications management procedures. An example method performed by a user equipment includes transmitting, to a base station (BS), UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE and receiving, from the BS based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an ML model training procedure. A network entity may receive a trigger to activate an ML model training procedure based on at least one of an indication from an ML model repository or a protocol of the network entity. The network entity may transmit an ML model training request to activate the ML model training at one or more nodes. The one or more nodes may be associated with a RAN that may transmit, based on receiving the ML model training request, ML model training results indicative of a trained ML model. In aspects, an apparatus, such as a UE, may train the ML model based on an ML model training configuration received from the RAN, and transmit an ML model training report indicative of the trained ML model.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a UE capability for AI/ML. A UE may receive a request from a network to report a UE capability for at least one of an AI procedure or an ML procedure. The UE may transmit to the network, based on the request to report the UE capability, an indication of one or more of an AI capability, an ML capability, a radio capability associated with the at least one of the AI procedure or the ML procedure, or a core network capability associated with the at least one of the AI procedure or the ML procedure.

Abstract: Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.