DE/PSO-aided Hybrid Linear Detectors for MIMO-OFDM Systems under Correlated Arrays

TL;DR

This paper evaluates hybrid linear detectors aided by evolutionary heuristics like DE and PSO for MIMO-OFDM systems, demonstrating performance improvements and analyzing complexity and correlation effects.

Contribution

It introduces hybrid detection schemes combining linear detectors with heuristic algorithms, showing enhanced performance and reduced complexity in correlated MIMO-OFDM scenarios.

Findings

Hybrid detectors outperform traditional MMSE and MF detectors.

Performance improves with better initial solutions from linear detectors.

Algorithm complexity scales with the number of antennas.

Abstract

In this paper, we analyze the performance of evolutionary heuristic-aided linear detectors deployed in Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency-Division Multiplexing (OFDM) systems, considering realistic operating scenarios. Hybrid linear-heuristic detectors under different initial solutions provided by linear detectors are considered, namely differential evolution (DE) and particle swarm optimization (PSO). Numerical results demonstrated the applicability of hybrid detection approach, which can improve considerably the performance of minimum mean-square error (MMSE) and matched filter (MF) detectors. Furthermore, we discuss how the complexity of the presented algorithms scales with the number of antennas, besides of verifying the spatial correlation effects on MIMO-OFDM performance assisted by linear, heuristic and hybrid detection schemes. The influence of the initial point in the performance improvement and complexity reduction is evaluated numerically.