A communication
system for conveying information from an information source across a communications channel using a joint source channel coding
autoencoder, comprising: an
encoder neural network of the joint source channel coding
autoencoder, the
encoder neural network having: an input layer having input nodes corresponding to a sequence of source symbols Sm={S1, S2, . . . , Sm}, the Si, taking values in an alphabet S, received at the input layer from the information source as samples thereof, and a channel input layer coupled to the input layer through one or more neural network
layers, the channel input layer having nodes
usable to provide values for the Xi, of a channel input vector Xn={X1, X2, . . . , Xn}, the Xi, taking values from the available input
signal alphabet X of the communications channel, the channel input vector Xn comprising a plurality of
signal values Xp
usable to reconstruct an information source, wherein the number p of the plurality of
signal values Xp is smaller than the total number n of signal values of the channel input vector Xn, and wherein at least one of the remaining signal values of the channel input vector Xn is
usable to increase the quality of the reconstructed information source, and wherein the
encoder neural network is configured through training to be usable to map sequences of source symbols Sm received from the information source directly to a representation as a channel input vector Xn, usable to drive a
transmitter to transmit a corresponding signal over the communications channel; a first decoder neural network and a second decoder neural network of the joint source channel coding
autoencoder, each decoder neural network having: a channel output layer having nodes corresponding to a channel output vector Y received from a
receiver receiving a signal corresponding to at least the plurality of signal values Xp of the channel input vector Xn transmitted by the
transmitter and transformed by the communications channel, and an output layer coupled to the channel output layer through one or more neural network
layers, having nodes matching those of the input layer of the encoder neural network, wherein the first decoder neural network is configured through training to map the representation of the source symbols as the channel output vector Y transformed by the communications channel to a reconstruction of the source symbols Ŝm output from the output layer of the joint source channel coding autoencoder, the reconstruction of the source symbols Ŝm being usable to reconstitute the information source; and wherein the number of signal values of the channel output vector Y received by the first decoder network is more than the number of signal values of the channel output vector Y received by the second decoder neural network.