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Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

This paper aims to develop an efficient compressed sensing (CS) based channel estimation method for non-sample spaced sparse channels in orthogonal frequency division multiplexing (OFDM) systems, which can effectively balance the channel estimation performance, spectral efficiency and computational complexity. To realize this goal, a novel delay tracking and residual norm minimization (DT-RNM) method is proposed. In this method, the idea of reference delay grids (RDG) inspired by the effective local delay tracking (DT) is formulated, which is proven to be much more efficient than current algorithms in estimating non-sample spaced sparse channels. Both theoretical derivation and simulation results show the effectiveness of the proposed method.