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This paper investigates the sparse channel estimation issue of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Beginning with the formulation of least squares (LS) solution to sparse MIMO-OFDM channel estimation, a compressed channel sensing (CCS) framework based on the new smoothed l0-norm-regularized least squares (l2-Sl0) algorithm is proposed. Three methods, namely quasi-Newton, conjugate gradient, and optimization in the null and complement spaces of the measurement matrix, are then proposed to solve the l2-Sl0 unconstrained optimization problem. Moreover, the two former are also applied to solve the l2-Sl0 channel estimation. A number of computer simulation-based experiments are conducted showing a better reconstruction accuracy of the l2-Sl0 algorithm as compared with the smoothed l0-norm (Sl0) algorithm in the presence of noise. The proposed CCS approach can save nearly 25% pilot signals to maintain the same mean square error (MSE) and bit error rate (BER) performances as given by the conventional LS method. © 2013 Stolojescu-Crisan and Isar; licensee Springer.

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing