Novel Compressed Sensing-Based Channel Estimation Algorithm and Near-Optimal Pilot Placement Scheme

Abstract

This paper presents a novel recovery algorithm based on sparsity adaptive matching pursuit (SaMP) and a new near-optimal pilot placement scheme, for compressed sensing (CS)-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) communication systems. Compared with other state-of-the-art recovery algorithms, the proposed algorithm possesses the feature of SaMP of not requiring a priori knowledge of the sparsity level, and moreover, adjusts the step size adaptively to approach the true sparsity level. Furthermore, we focus on the pilot pattern design in sparse channel estimation. Although a brute-force search guarantees the optimal pilot pattern, it is prohibitive to examine all possibilities due to high computational complexity. It is known that by minimizing the mutual coherence of the measurement matrix when the signal is sparse on the unitary discrete Fourier transform (DFT) matrix, the optimal set of pilot locations is a cyclic difference set (CDS). Based on this, we propose an efficient near-optimal pilot placement scheme in cases where CDS does not exist. Simulation results show that the proposed channel estimation algorithm, with the new pilot placement scheme, offers a better tradeoff between the performance-in terms of mean-squared-error (MSE) and bit-error-rate (BER)-and complexity, when compared to other estimation algorithms.