DE/PSO-aided Hybrid Linear Detectors for MIMO-OFDM Systems under Correlated Arrays
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.
Keywords
Cite
@article{arxiv.1810.01600,
title = {DE/PSO-aided Hybrid Linear Detectors for MIMO-OFDM Systems under Correlated Arrays},
author = {Rafael Masashi Fukuda and David William Marques Guerra and Ricardo Tadashi Kobayashi and Taufik Abrao},
journal= {arXiv preprint arXiv:1810.01600},
year = {2018}
}
Comments
19 pages, 7 figures and 5 tables