Abstract: Disclosed are techniques for communication. In an aspect, a network component determines that a level of heterogeneity associated with a network layer of a hierarchical network layer arrangement with multiple network layers is above a threshold. In some designs, the heterogeneity level determination may be based on heterogeneity metric(s) reported by device(s) associated with the network layer. The network component may transmit, to at least one device associated with the network layer in response to the heterogeneity level determination, a data reporting instruction associated with the network layer. The network component receives data associated with the network layer from the at least one device in accordance with the data reporting instruction.

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: 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: Aspects described herein relate to using machine learning (ML) models for performing channel state information (CSI) encoding or decoding, CSI-reference signal (RS) optimization, channel estimation, etc. The ML models can be trained by a user equipment (UE), separately by the UE and a network node (e.g., base station), or jointly by the UE and network node.

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: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may encode channel state feedback (CSF) information to compress the CSF information to a first encoding output associated with a first dimensional space, and apply entropy coding to the first encoding output of the channel state feedback information. The entropy coding may transform the first encoding output to a second encoding output associated with a second dimensional space that is smaller than the first dimensional space of the first encoding output. The UE may transmit a CSF message comprising the second encoding output. A network device may receive the CSF message and apply entropy decoding to the compressed CSF information to partially decompress the compressed CSF information to a first decoding output. The network device may decode the first decoding output to completely decompress the compressed CSF information to a second decoding output.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, based at least in part on one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication, assistance information associated with an expected performance of the AI model. The UE may assess, based at least in part on the assistance information, the expected performance of the AI model. Numerous other aspects are described.

Abstract: A method of wireless communication by a user equipment (UE) includes receiving, from a network device, a request to initiate gradient computations for a round of federated learning. The method also includes computing gradients in response to receiving the request to initiate gradient computation. The method further includes informing the network device of availability of the gradients. The method still further includes receiving, from the network device, information to enable transfer of the gradients to the network device. The method further includes transferring the gradients to the network device in response to receiving the information to enable transfer.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit a UE capability reporting message including information associated with identifying a set of UE conditions associated with a first set of functionalities, wherein the first set of functionalities corresponds to a set of model features. The UE may receive, based at least in part on transmitting the UE capability reporting message, control signaling identifying a second set of functionalities that is a subset of the first set of functionalities. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for over-the-air (OTA) aggregation of data. Certain techniques include transmitting, to a plurality of user equipments (UEs), a first reference signal (RS) via a first transmit beam of a base station (BS), wherein the plurality of UEs and the BS share a global federated learning model: receiving, in response to the first RS, a first set of signals each carrying corresponding local gradient information of a first set of local gradient information for the global federated learning model, the first set of local gradient information comprising local gradient information calculated by each UE of multiple UEs of the plurality of UEs, the first set of local gradient information received via a first receive beam of the BS; and aggregating, in an analog domain, the first set of signals to aggregate the first set of local gradient information received from the multiple UEs.

Abstract: A method of wireless communication by a user equipment (UE) includes generating, by an upper analog media access control (MAC-A) layer of a protocol stack, a data packet with a header and a data field. The header indicates a neural network identifier (ID) and a request ID. The data field includes gradient data for a federated learning iteration. The method also includes transferring the data packet to lower layers of the protocol stack for transmission to a network device across a wireless network.

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: A protocol stack architecture for processing machine learning (ML) data includes a ML layer to manage ML data communication with a network device. The ML layer is coupled to multiple ML training blocks, and ML and inference blocks for multiple neural networks, and an analog data communications stack coupled to the ML layer. The analog data communications stack has an upper media access control analog (MAC-A) layer coupled to the ML layer and configured to store data for each neural network, a lower MAC-A layer coupled to the upper MAC-A layer and configured to segment and reassemble analog ML data, and an analog physical layer coupled to the lower MAC-A layer and configured to communicate analog data with the network device. The architecture includes a digital data communications stack coupled to the ML layer and the lower MAC-A layer and configured to manage digital communications with the network device.

Abstract: Systems and techniques are disclosed for performing wireless communications. For example, a wireless device (e.g., a user equipment (UE)) can transmit (or output for transmission), to a network entity, capability information related to a first functionality supported by a set of machine learning (ML) models of the apparatus. The wireless device can receive, from the network entity, a performance target associated with the first functionality.

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: A parameter server located at a base station may coordinate federated learning among multiple user equipment (UEs) using over-the-air (OTA) aggregation with power control to mitigate aggregation distortion due to amplitude misalignment. The parameter server may select a first group of UEs for a first OTA aggregation session of a federated learning round based on a common received power property of each UE in the first group of UEs. The parameter server may transmit a global model to the first group of UEs. Each UE in the first group may train the global model based on a local dataset and transmit values associated with the trained local model. The parameter server may receive, on resource elements for the first group of UEs, a first aggregate amplitude modulated analog signal representing a combined response from the first group of UEs.

Abstract: Certain aspects of the present disclosure provide techniques for semantic communication. A method for wireless communications includes obtaining, by a semantic encoder, a set of real values for transmission to a receiving device; encoding, by the semantic encoder, the set of real values based on a semantic model and a first dimension of a set of dimensions, wherein each different dimension in the set of dimensions corresponds to a different number of real values to output; outputting an encoded set of real values; outputting the encoded set of real values for transmission to the receiving device over a wireless communication channel; obtaining feedback from the receiving device; and using a second dimension of the set of dimensions based on the feedback.

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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit an indication of functionalities associated with artificial intelligence or machine learning (AI/ML) models supported by the UE. The UE may receive one or more AI/ML models associated with the functionalities. Numerous other aspects are described.

Abstract: An apparatus, method and computer-readable media are disclosed for performing wireless communications. For example, a process for wireless communications is provided. The process can include receiving a first set of operations supported by one or more machine learning models of a network entity, receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters are supported by the one or more machine learning models of the network entity, selecting a machine learning model for performing a first operation of the first set of operations based on the first set of parameters, detecting a change in at least one of: the first operation, or a parameter associated with the first operation, and transmitting an indication to change the first operation based on the detected change.