Hybrid message-passing-algorithm-based deep learning neural network for a massive beam-forming system

Abstract

A multi-beam transmission method is provided for transmitting using an N-beam transmitter to a receiver having K receive beams. In the transmitter, a non-linear encoder implemented by a machine learning block, and a linear encoder are trained using gradient descent back propagation that relies on feedback from the receiver. For each input to be transmitted the machine learning block is used to process the input to produce N/K sets of L outputs. The linear encoder is used to perform linear encoding on each set of L outputs to produce a respective set of K outputs so as to produce N/K sets of K encoded outputs overall and N encoded outputs overall. One of the N/K sets of K outputs from each set of K beams. In order to allow a generalization to different SNRs, and to allow for time varying channel conditions, the non-linear layers, implemented in the non-linear encoder, are responsible for extracting features and regenerating the features, while the linear layers implemented in the linear encoder, are responsible for more generalization.