Linear Precoding for Relay Networks with Finite-Alphabet Constraints

TL;DR

This paper proposes a linear precoding scheme for relay networks with finite-alphabet constraints, optimizing mutual information and demonstrating significant performance gains over traditional methods.

Contribution

It introduces a novel iterative algorithm based on convex optimization and manifold theory to optimize precoding for practical relay networks with finite constellations.

Findings

The proposed algorithm outperforms conventional precoding methods.

Significant mutual information gains are achieved in numerical simulations.

The structure of the optimal precoding matrix is exploited for improved design.

Abstract

In this paper, we investigate the optimal precoding scheme for relay networks with finite-alphabet constraints. We show that the previous work utilizing various design criteria to maximize either the diversity order or the transmission rate with the Gaussian-input assumption may lead to significant loss for a practical system with finite constellation set constraint. A linear precoding scheme is proposed to maximize the mutual information for relay networks. We exploit the structure of the optimal precoding matrix and develop a unified two-step iterative algorithm utilizing the theory of convex optimization and optimization on the complex Stiefel manifold. Numerical examples show that this novel iterative algorithm achieves significant gains compared to its conventional counterpart.