MIND: Maximum Mutual Information Based Neural Decoder

TL;DR

This paper introduces MIND, a neural decoder that optimizes detection in communication systems by estimating mutual information, capable of handling unknown channels and providing performance metrics.

Contribution

It proposes a novel neural estimator for mutual information and a neural decoding architecture that works with unknown channels and estimates decoding performance.

Findings

MIND effectively decodes in unknown channels.

It estimates the mutual information achieved.

It compares favorably with MAP and ML decoding strategies.

Abstract

We are assisting at a growing interest in the development of learning architectures with application to digital communication systems. Herein, we consider the detection/decoding problem. We aim at developing an optimal neural architecture for such a task. The definition of the optimal criterion is a fundamental step. We propose to use the mutual information (MI) of the channel input-output signal pair, which yields to the minimization of the a-posteriori information of the transmitted codeword given the communication channel output observation. The computation of the a-posteriori information is a formidable task, and for the majority of channels it is unknown. Therefore, it has to be learned. For such an objective, we propose a novel neural estimator based on a discriminative formulation. This leads to the derivation of the mutual information neural decoder (MIND). The developed neural architecture is capable not only to solve the decoding problem in unknown channels, but also to return an estimate of the average MI achieved with the coding scheme, as well as the decoding error probability. Several numerical results are reported and compared with maximum a-posteriori and maximum likelihood decoding strategies.