Joint Channel Estimation and Multi-user Detection for OFDMA Systems Using a Genetic Algorithm with Simulated Annealing-Based Mutation

In orthogonal frequency division multiple access (OFDMA) system, the multiple access interference (MAI) is a critical factor that significantly degrades system performance. In this research, a genetic algorithm (GA) is employed for joint channel estimation and multi-user detection. For improving the weakness of GA in exploitation, an approach called simulated annealing mutated-GA (SAM-GA) is considered by embedding the simulated annealing (SA) to the mutation operation of GA. Experimental result demonstrates that the proposed SAM-GA scheme achieves the best performance in terms of mean squared error (MSE) and bit error rate (BER) for joint channel estimation and multi-user detection in the OFDMA system as compared to other existing schemes.