Determine Outperformance of Hybrid GA–PSO vs PSO for LS Channel Estimation in OFDM MIMO

Determine whether the hybrid Genetic Algorithm–Particle Swarm Optimization (GA–PSO) approach for optimizing the Least Squares (LS) channel estimator in OFDM MIMO systems achieves lower mean squared error than optimizing LS with standard Particle Swarm Optimization across signal-to-noise ratio values.

Background

Orthogonal Frequency Division Multiplexing (OFDM) combined with Multiple Input Multiple Output (MIMO) requires accurate channel state information, commonly estimated using the Least Squares (LS) or Minimum Mean Square Error (MMSE) methods. Because MMSE has higher computational complexity, LS is often optimized using meta-heuristics such as Particle Swarm Optimization (PSO).

The paper proposes a hybrid GA–PSO algorithm to optimize LS channel estimation and compares its mean squared error (MSE) performance against LS, MMSE, and PSO across different signal-to-noise ratio (SNR) values. The authors report that while the hybrid method performs better at some SNRs and converges in fewer iterations in certain regimes, they cannot definitively assert that it outperforms PSO overall in MSE, leaving the comparative outperformance unresolved.