Joint CFO and sparse channel estimation for MIMO-OFDM systems via the SAGE algorithm

In this paper, we investigate the problem of joint carrier frequency offset (CFO) and sparse channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The conventional channel estimation methods using least-squares (LS) fail to exploit the sparsity of wireless channel, which results in a poor performance and a long training sequence in a sparse scenario. The proposed algorithm uses subspace pursuit instead. Furthermore, an iterative space-alternation generalized expectation-maximization (SAGE) estimator is proposed to provide a solution to the complex multi-dimensional extreme-value search problem. We also derive the Cramér-Rao lower bound (CRLB) for multiple parameters estimation. Simulation results show that the proposed algorithm achieves a better performance compared with the conventional iterative joint algorithms.