Joint Constellation Shaping Using Gradient Descent Approach for MU-MIMO Broadcast Channel

TL;DR

This paper presents a learning-based joint constellation shaping method for multi-user MIMO broadcast channels that optimizes mutual information without relying on traditional linear precoding or SIC, improving communication efficiency.

Contribution

It introduces a novel gradient descent approach to jointly optimize constellations for MU-MIMO channels, avoiding superposition coding and successive interference cancellation.

Findings

Achieves higher mutual information compared to linear precoders.

Optimizes constellation design for each user independently.

Demonstrates effectiveness in multi-user MIMO scenarios.

Abstract

We introduce a learning-based approach to optimize a joint constellation for a multi-user MIMO broadcast channel ($T$ Tx antennas, $K$ users, each with $R$ Rx antennas), with perfect channel knowledge. The aim of the optimizer (MAX-MIN) is to maximize the minimum mutual information between the transmitter and each receiver, under a sum-power constraint. The proposed optimization method do neither impose the transmitter to use superposition coding (SC) or any other linear precoding, nor to use successive interference cancellation (SIC) at the receiver. Instead, the approach designs a joint constellation, optimized such that its projection into the subspace of each receiver $k$, maximizes the minimum mutual information $I(W_k;Y_k)$ between each transmitted binary input $W_k$ and the output signal at the intended receiver $Y_k$. The rates obtained by our method are compared to those achieved with linear precoders.