A receiver for use in a data channel on an integrated circuit device includes a non-linear equalizer having as inputs digitized samples of signals on the data channel, decision circuitry configured to determine from outputs of the non-linear equalizer a respective value of each of the signals, and adaptation circuitry configured to adapt parameters of the non-linear equalizer based on respective ones of the value. The non-linear equalizer may be a neural network equalizer, such as a multi-layer perceptron neural network equalizer, or a reduced complexity multi-layer perceptron neural network equalizer. A method for detecting data on a data channel on an integrated circuit device includes performing non-linear equalization of digitized samples of input signals on the data channel, determining from output signals of the non-linear equalization a respective value of each of the output signals, and adapting parameters of the non-linear equalization based on respective ones of the value.

A detector coherent-receives a signal being transmitted from a transmitter. A filter group includes a plurality of filters connected in series along a signal path of a reception signal. The plurality of filters include a plurality of non-linear distortion compensation filters and one or more linear distortion compensation filters. A coefficient updating unit controls a filter coefficient of the plurality of non-linear distortion compensation filters and a filter coefficient of at least some of the linear distortion compensation filters. The coefficient updating unit adaptively controls the filter coefficient, by using an error back propagation method, based on a difference between an output signal being output from the filter group and a predetermined value of the output signal.

The present invention discloses a method for the design of complex-valued channel equalizer of digital communication systems, including: constructing a channel equalizer by using a complex-valued neural network; collecting the output signal y(n)=[y(n), y(n−1), . . . , y(n−m+1)].sup.T of the nonlinear channel of the underlying digital communication system as the input of complex-valued neural network and s(n−τ) as the desired output, and taking the mean squared error as the loss function for the training of complex-valued neural network, which is optimized by the proposed adaptive complex-valued L-BFGS algorithm, and finally using it to implement the design of channel equalizer for digital communication systems. The present invention proposes the use of a multi-layer feedforward complex-valued neural network to construct complex-valued channel equalizer. A new adaptive complex-valued L-BFGS algorithm is proposed for efficient training of complex-valued neural network, which is eventually applied to facilitate the design of the channel equalizer for digital communication systems.

An apparatus, method and computer program is described including: receiving, at a receiver of a transmissions system, transmitted signals from each of a plurality of transmitters, wherein each transmitter communicates with the receiver over one of a plurality of channels of the transmission system, wherein each transmitter includes a transmitter algorithm having at least some trainable weights, wherein each transmitter algorithm has the same trainable weights and wherein each of the transmitted signals is based on a perturbed channel symbol generated at the respective transmitter, wherein the channel symbols and perturbations are known to the receiver; updating the weights of the transmitter algorithm, at the receiver, based on a loss function; providing the updated weights to each transmitter of the transmission system; and repeating the receiving and updating until a first condition is reached.

A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

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.

Embodiments of this application disclose a neural network-based communication method and a related apparatus. Specifically, joint training optimization is performed on an encoding neural network used by a transmit end and a decoding neural network used by a receive end. A first neural network in the encoding neural network reuses the decoding neural network and a parameter of the decoding neural network.

In some examples, a node for a telecommunication network includes a neural-network-based receiver for uplink communications. The node is configured to modify the neural-network-based receiver to generate a set of modified receiver frameworks defining respective different versions for the receiver, using each of the modified receiver frameworks, generate respective measures representing bits encoded by a signal received at the node, calculate a value representing a variance of the measures, and on the basis of the value, determine whether to select the signal received at the node for use as part of a training set of data for the neural-network-based receiver.

Methods, systems, and devices for wireless communications are described. In some systems, a device, such as an internet of things (IoT) device, may include a configuration or software (e.g., in baseband) that is common for multiple applications of the device. In some aspects, the device may select a setting for at least some if not each of a set of parameters associated with or defining a device profile of the device based on an application of the device. The device may perform a mapping procedure to map the settings for the parameters associated with the device profile to one or more baseband configurations or baseband handles and the device may customize the baseband of the device using the one or more baseband configurations. As such, the device may operate or communicate using the baseband that is customized based on the device profile and application of the device.

An apparatus, method and computer program is described comprising: initialising trainable parameters of a transmission system having a transmitter, a channel and a receiver; generating training symbols on the basis of a differentiable distribution function; transmitting modulated training symbols to the receiver over the channel in a training mode; generating a loss function based on the generated training symbols and the modulated training symbols as received at the receiver of the transmission system; and generating updated parameters of the transmission system in order to minimise the loss function.