Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data delayed versions of at least a portion of the respective processing results with coefficient data specific to a processing mode selection. For example, a computing system with processing units may mix the input data delayed versions of respective outputs of various layers of multiplication/accumulation processing units (MAC units) for a transmission in a radio frequency (RF) wireless domain with the coefficient data to generate output data that is representative of the transmission being processed according to a wireless processing mode selection. In another example, such mixing input data with delayed versions of processing results may be to receive and process noisy wireless input data. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.

The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.

A method and system for training a neural network are herein provided. According to one embodiment, a method includes generating a first labelled dataset corresponding to a first modulation scheme and a second labelled dataset corresponding to a second modulation scheme, determining a first gradient of a cost function between a first neural network layer and a second neural network layer based on back-propagation using the first labelled dataset and the second labelled dataset, and determining a second gradient of the cost function between the second neural network layer and a first set of nodes of a third neural network layer based on back-propagation using the first labelled dataset. The first set of nodes of the third neural network layer correspond to a first plurality of detector classes associated with the first modulation scheme.

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.

A method and apparatus of distortion compensation during data transmission uses an interweaved look-up table (ILUT) to mitigate residual signal distortions in a signal transmitted over a transmission link. The ILUT interweaves states across both an I and a Q tributary to calculate mean error and an extended symbol basis. As a result, the method works particularly well against two-dimensional distortions like nonlinearity, IQ-imbalance, and quadrature error. The method may be used for either pre-compensation when it is combined with k-means clustering in a transmitter or post-compensation when it is combined with maximum likelihood (ML) detection in a receiver.

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.

A system and a method may include a receiver circuit configured to receive an encoded codeword over a channel from an encoder neural network of an encoder, and a decoder circuit, including a decoder neural network, configured to decode the encoded codeword, and generate a list of decoded message words, the list of the decoded message words including a plurality of candidate message words representing the message word.

A communications system and method is described comprising: handshaking between a transmitter and a receiver of the communication system to initiate a training procedure, wherein said handshaking comprises a training setup request message comprising parameters for the training procedure, wherein the transmitter comprises trainable parameters and/or the receiver comprises trainable parameters; receiving identified training data from the transmitter at the receiver, wherein the training data comprises transmitter training data and/or receiver training data; sending training information from the receiver to the transmitter, wherein the training information comprises information for controlling training at the transmitter and/or the receiver; and terminating the training procedure.

A method and system for maximizing throughput and minimizing latency in a communication system that supports heterogeneous links is presented. The communication system supports a primary link and an alternate link, and the method and system leverage the alternate link to reduce the overhead transmitted over the primary link, thereby increasing throughput and reducing end-to-end latency. The higher latency alternate link provides a delayed version of an information signal that corresponds to a portion of the information signal that is transmitted on the primary link. The received samples from the primary and alternate links may be used to equalize subsequent portions of the information signal received over the primary link, and may also be used for synchronization, timing recovery, DC offset removal, I/Q imbalance compensation, and frequency-offset estimation.

Examples described herein include methods, devices, and systems which may compensate input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN may calculate an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data may also be transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path may be deactivated and the receiver stage may receive an additional RF wireless transmission to be processed in the receiver stage.