In a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE), a method of a first terminal in a wireless communication system is disclosed and may include performing channel measurement, based on one or more first reference signals received from a base station; identifying channel distribution information between the first terminal and the base station, based on the measured channel; selecting one or more representative channel vectors (RCVs), based on the identified channel distribution information; generating one or more constellations corresponding to the selected one or more RCVs; transmitting constellation set information including the generated one or more constellations to the base station; and performing communication with the base station, based on the generated one or more constellations.

This specification relates to end-to-end learning in communication systems and describes: organising a plurality of transmitter neutral networks and a plurality of receiver neural networks into a plurality of transmitter-receiver neural network pairs, wherein a transmitter-receiver neural network pair is defined for each of a plurality of subcarrier frequency bands of a multi-carrier transmission system; arranging a plurality of symbols of the multi-carrier transmission system into a plurality of transmit blocks; mapping each of said transmit blocks to one of the transmitter-receiver neural network pairs; transmitting each symbol using the mapped transmitter-receiver neural network pair; and training at least some weights of the transmit and receive neural networks using a loss function for each transmitter-receiver neural network pair.

An apparatus and method is described including obtaining or generating a transmitter-training sequence of messages for a transmission system, wherein the transmission system includes a transmitter, a channel and a receiver, wherein the transmitter includes a transmitter algorithm having at least some trainable weights and the receiver includes a receiver algorithm having at least some trainable weights; transmitting perturbed versions of the transmitter-training sequence of messages over the transmission system; receiving first receiver loss function data at the transmitter, the first receiver loss function data being generated based on a received-training sequence as received at the receiver and knowledge of the transmitter training sequence of messages for the transmission system; and training at least some weights of the transmitter algorithm based on first receiver loss function data and knowledge of the transmitter-training sequence of messages and the perturbed versions of the transmitter-training sequence of messages.

A device and a method for training a model are disclosed, wherein the method of training the model includes: first classifying a plurality of data packets using the model, wherein a first class is assigned to each data packet of a plurality of data packets, wherein the first class is associated with a receiver of a plurality of receivers; second classifying the plurality of data packets, wherein a second class is assigned to each data packet of the plurality of data packets, wherein the second class is associated with a receiver of the plurality of receivers; and training the model using the plurality of first classes and the plurality of second classes assigned to the plurality of data packets.

The present disclosure relates to encoding methods and apparatus, and decoding methods and apparatus. In one example encoding method, first input information is obtained. The first input information is encoded based on an encoding neural network to obtain and output first output information. The encoding neural network comprises a first neuron parameter, and the first neuron parameter is used to indicate a mapping relationship between the first input information and the first output information.

A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.

A digital circuit for implementing a channel-tracking functionality, in which an adaptive (e.g., FIR) filter is updated based on reinforcement learning. In an example embodiment, the adaptive filter may be updated using an LMS-type algorithm. The digital circuit may also include an electronic controller configured to change the convergence coefficient of the LMS algorithm using a selection policy learned by applying a reinforcement-learning technique and based on residual errors and channel estimates received over a sequence of iterations. In some embodiments, the electronic controller may include an artificial neural network. An example embodiment of the digital circuit is advantageously capable of providing improved performance after the learning phase, e.g., for communication channels exhibiting variable dynamicity patterns, such as those associated with aerial copper cables or some wireless channels.

Techniques and apparatuses are described for machine-learning architectures for simultaneous connection to multiple carriers. In implementations, a network entity determines at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. At times, the at least one DNN configuration includes a first portion for forming a first DNN at the network entity, and a second portion for forming a second DNN at the UE. The network entity forms the first DNN based on the first portion and communicates an indication of the second portion to the UE. The network entity directs the UE to form the second DNN based on the second portion, and uses the first DNN to exchange, over the wireless communication system, the information with the UE using the carrier aggregation.

Various embodiments may employ neural networks at transmitting devices to compress transmit (TX) waveform distortion. In various embodiments, compressed TX waveform distortion information may be conveyed to a receiving device. In various embodiments, the signaling of TX waveform distortion information from a transmitting device to a receiving device may enable a receiving device to mitigate waveform distortion in a transmit waveform received from the transmitting device. Various embodiments include systems and methods of wireless communication by transmitting a waveform to a receiving device performed by a processor of a transmitting device. Various embodiments include systems and methods of wireless communication by receiving a waveform from a transmitting device performed by a processor of a receiving device.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.