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In wireless communication, multiple input multiple output-orthogonal frequency division multiplexing (MIMO–OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error ( MMSE ). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. While comparing with LS and MMSE method individually, the combined LS and MMSE method using evolutionary programming can greatly reduce the error. If the error is reduced to a very low value, then an exact signal will be received. Thus, we propose a hybrid technique that includes particle swarm optimization (PSO) and genetic algorithm (GA) for channel estimation in MIMO–OFDM systems. The technique performs the conventional LS and MMSE channel estimation followed by enabling a fine tuning on the obtained channel model. The PSO and GA contribute in fine tuning the obtained channel model so that the channel model is derived further to correlate with the ideal model. The result shows the performance of the proposed method is better than LS and MMSE method in all the mutation and crossover values and also in all the iterations computed. The performance of OFDM systems using proposed technique can be observed from the imitation and relative results.
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