Structured-Compressed-Sensing-Based Impulsive Noise Cancelation for MIMO Systems

In this paper, the powerful signal processing theory of structured compressed sensing (SCS) is exploited to overcome the challenge of impulsive noise (IN) cancelation in multiple-input multiple-output (MIMO) systems. To the best of the authors' knowledge, the SCS theory is adopted for the first time for IN elimination, bridging IN mitigation and MIMO systems for its potential applications in vehicular-related communications. In achieving the SCS-based IN cancelation, the measurements matrix of the IN is first obtained from the null subcarriers in the MIMO system specified by the IEEE 802.11 standards series. The SCS optimization framework is then formulated through the proposed spatially multiple measuring method, by fully exploiting the spatial correlation of the IN signals at different receive antennas. To efficiently reconstruct the IN signal, an enhanced SCS-based greedy algorithm, structured a priori aided sparsity adaptive matching pursuit, is proposed, which significantly improves the accuracy and robustness compared with the state-of-the-art methods. Theoretical analysis is presented to guarantee the convergence and the performance error bound of the proposed greedy algorithm. Computer simulations validate that the proposed scheme outperforms the conventional ones over the wireless MIMO channel.