  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.
