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Orthogonal Frequency Division Multiplexing (OFDM) is a well-known technique used in modern wide band wireless communication systems. Coherent OFDM systems achieve its advantages over a multipath fading channel, if channel impulse response is estimated precisely at the receiver. Pilot-aided channel estimation in wide band OFDM systems adopts the recently explored compressive sensing technique to decrease the transmission overhead of pilot subcarriers, since it exploits the inherent sparsity of the wireless fading channel. The accuracy of compressive sensing techniques in sparse channel estimation is based on the location of pilots among OFDM subcarriers. A sufficient condition for the optimal pilot selection from Sylow subgroups is derived. A Sylow subgroup does not exist for most practical OFDM systems. Therefore, a deterministic pilot search algorithm is described to select pilot locations based on minimizing coherence, along with minimum variance. Simulation results reveal the effectiveness of the proposed algorithm in terms of bit error rate, compared to the existing solutions.

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-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.

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