Adaptive channel coding using machine-learned models

Abstract

The present disclosure provides systems and methods that enable adaptive training of a channel coding model including an encoder model, a channel model positioned structurally after the encoder model, and a decoder model positioned structurally after the channel model. The channel model can have been trained to emulate a communication channel, for example, by training the channel model on example data that has been transmitted via the communication channel. The channel coding model can be trained on a loss function that describes a difference between input data input into the encoder model and output data received from the decoder model. In particular, such a loss function can be backpropagated through the decoder model while modifying the decoder model, backpropagated through the channel model while the channel model is held constant, and then backpropagated through the encoder model while modifying the encoder model.

Description

technical field [0001] This disclosure relates generally to machine learning. More specifically, the present disclosure relates to systems and methods for implementing adaptive channel coding using machine learning models. Background technique [0002] Machine learning generally refers to the field of computer science that focuses on enabling machines such as computers to learn without being explicitly programmed. Machine learning involves the study and construction of machine-implemented algorithms or techniques that enable machines to learn from and make predictions about data. Specifically, such algorithms can operate by building models from a training set of input observations in order to make data-driven predictions or decisions represented as outputs, rather than strictly following static programming instructions. [0003] A major branch of machine learning techniques includes supervised learning techniques. Supervised learning can involve inferring or learning a fu...

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Problems solved by technology

Therefore, it may be difficult or impossible to perform supervised learning in such scenarios

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