Low-Complexity MIMO Precoding for Finite-Alphabet Signals

TL;DR

This paper introduces a low-complexity precoding method for MIMO channels with finite-alphabet signals that leverages statistical channel information, significantly reducing computational complexity while maintaining near-optimal mutual information performance.

Contribution

It proposes a novel precoding structure and an iterative algorithm that efficiently maximize mutual information using only statistical CSI, suitable for large antenna arrays and high-order constellations.

Findings

Reduces computational complexity of precoding algorithms

Supports larger antenna arrays and signal alphabets

Maintains near-optimal mutual information performance

Abstract

This paper investigates the design of precoders for single-user multiple-input multiple-output (MIMO) channels, and in particular for finite-alphabet signals. Based on an asymptotic expression for the mutual information of channels exhibiting line-of-sight components and rather general antenna correlations, precoding structures that decompose the general channel into a set of parallel subchannel pairs are proposed. Then, a low-complexity iterative algorithm is devised to maximize the sum mutual information of all pairs. The proposed algorithm significantly reduces the computational load of existing approaches with only minimal loss in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unmanageable with existing solutions. Most importantly, the proposed solution does not require instantaneous channel state information (CSI) at the transmitter, but only statistical CSI.