Structured Compressed Sensing Based NBI Recovery for MIMO WLAN Systems

Narrowband interference (NBI) is a major concern that constrains the performance and development of wireless local area network (WLAN) systems. Compressed sensing (CS) is a new and powerful signal processing technique that is applied to solve this problem in this paper. In the framework of structured CS (SCS), we investigated the multiple input multiple output (MIMO) WLAN system, and proposed a novel NBI recovery method based on spatial multiple differential measuring (SMDM). At each receive antenna, a differential measurement vector is acquired from the repeated training sequences in the IEEE 802.11 preamble. Then multiple measurement vectors from each receive antenna are utilized for NBI recovery using the proposed SCS greedy algorithm, i.e. structured sparsity adaptive matching pursuit (S-SAMP). Simulation results indicate that the proposed scheme outperforms conventional ones over the MIMO wireless channel.