Performance of Compressive Sensing Technique for Sparse Channel Estimation in Orthogonal Frequency Division Multiplexing Systems

Orthogonal Frequency Division Multiplexing is a widely adopted multi carrier modulation in wireless communication systems due to its effective transmission and efficient bandwidth utilization ability. Wireless systems with coherent data detection require the estimation of channel at the receiver. Commonly employed pilot aided channel estimation probes the channel with known sequence called pilots and process the output to estimate the channel with linear reconsruction techniques like LS and MMSE. Wireless channels encountered in practice exhibits sparse structure that are having only a few dominant and many zeros coefficients. A recent development in Compressed Sensing (CS) has encouraged the extensive search on the application of sparse recovery algorithm to channel estimation. CS provides a constructive way to exploit the channel sparsity which reduces the number of pilots and hence increase spectral efficiency. Sparse channel estimation performed using sparse recovery algorithms provide better bit error rate compared with traditional LS and MMSE techniques. Further the quality of CS based sparse recovery algorithms depend on coherence of pilot structure therefore the pilot structure significantly affects the performance. To prove the efficacy of sparse recovery algorithm over pilot structure, performance of sparse recovery algorithm is evaluated for traditional combo and random pilot patterns generated. The random pilot with reduced mutual coherence has achieved better performance using sparse recovery algorithm.