Sparse channel estimation and measurement matrix optimization for zero padded SCBT system using compressed sensing

A sparse channel estimation method using compressed sensing (CS) is proposed for zero padded single carrier block transmission (ZP-SCBT) system employed in sparse wireless channels. We formulate the sparse channel estimation in ZP-SCBT system as a canonical CS problem, and optimize the measurement matrix by minimizing the mutual incoherence property (MIP), binary sequences with optimal aperiodic autocorrelation are utilized for constructing deterministic Toeplitz structured measurement matrices (TSMM) used for sparse channel estimation, which can greatly improve the estimation accuracy compared to Gaussian and Bernoulli distributed random TSMM. Numerical results show that the proposed sparse channel estimation method outperforms traditional least square (LS) estimation scheme when employed in sparse channels, and the channel estimation accuracy of the optimized deterministic TSMM outperforms Gaussian and Bernoulli distributed random TSMM.