Two-Dimensional Structured-Compressed-Sensing-Based NBI Cancelation Exploiting Spatial and Temporal Correlations in MIMO Systems

Narrowband interference (NBI) caused by narrowband licensed or unlicensed services is a major concern that constrains the performance of multiple-input multiple-output (MIMO) systems. In this paper, the new and powerful signal processing theory of structured compressed sensing (SCS) is introduced to solve this problem. Exploiting the 2-D spatial and temporal correlations of NBI in MIMO systems, a novel NBI recovery method, i.e., the spatial multiple differential measuring method, is proposed in the framework of 2-D SCS. At each receive antenna, a differential measurement vector is acquired from the repeated training sequences in the IEEE 802.11 series preamble. Then, multiple measurement vectors from all receive antennas are utilized to recover and cancel NBI using the proposed SCS greedy algorithm of structured sparsity adaptive matching pursuit. Simulation results indicate that the proposed scheme outperforms the conventional schemes over the wireless MIMO channel.