Adaptive channel estimation for STBC-OFDM systems based on nature-inspired optimization strategies
MACOM'10 Proceedings of the Third international conference on Multiple access communications
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Orthogonal frequency division multiplexing (OFDM) has attracted much attention in wireless communications due to its many advantages. However, OFDM is very sensitive to carrier frequency offset (CFO) which can destroy the orthogonality among subchannels. For OFDMA uplink systems with generalized carrier assignment scheme (GCAS), there are multiple CFOs need to be estimated simultaneously. While ML approach is recognized as one possible solution to this problem, its computational complexity is too high to be implemented for practical applications. To overcome this difficulty, this study employs particle swarm optimization (PSO) algorithms to estimate CFOs for OFDMA uplink systems with GCAS. Furthermore, a PSO scheme integrated with the mutation operator of genetic algorithm (GA) is proposed to improve the performance of conventional PSO algorithm. Experimental result indicates that the proposed hybrid PSO algorithm can achieve the same performance as ML scheme but with much less computational complexity.