Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l 0-norm-regularized least squares compressed sensing

摘要

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 l ₀-norm-regularized least squares ( l ₂- Sl ₀) 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 l ₂- Sl ₀ unconstrained optimization problem. Moreover, the two former are also applied to solve the l ₂- Sl ₀ channel estimation. A number of computer simulation-based experiments are conducted showing a better reconstruction accuracy of the l ₂- Sl ₀ algorithm as compared with the smoothed l ₀-norm ( Sl ₀) 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.