Probabilistic Shaping for the AWGN Channel

TL;DR

This paper investigates how probabilistic shaping, specifically using Maxwell-Boltzmann distributions and the Blahut-Arimoto algorithm, can optimize information transmission over AWGN channels, highlighting the limitations of standard approaches.

Contribution

It introduces a constrained Blahut-Arimoto algorithm to find optimal input distributions for maximizing mutual information at fixed SNRs.

Findings

Maxwell-Boltzmann distribution is not optimal for mutual information.

Standard Blahut-Arimoto algorithm does not yield the best mutual information.

Constrained Blahut-Arimoto algorithm improves mutual information optimization.

Abstract

In this report, we study communication over an additive white Gaussian noise channel with a fixed signal constellation. We measure how much information we can send through this channel and how to improve the rate of communication by changing the input probability distribution. More precisely, we study the mutual information obtained from the Maxwell-Boltzmann distribution, the Blahut-Arimoto algorithm and a constrained version of the Blahut-Arimoto algorithm. We emphasise the fact that the Maxwell--Boltzmann distribution is not optimal. We also observe that the Blahut-Arimoto algorithm does not give us the best mutual information over SNR. To get the optimal distribution for a fixed SNR, we have to implement a constrained version of the Blahut-Arimoto algorithm.