Dual Lawson Norm-Based Robust DOA Estimation for RIS-Aided Wireless Communication Systems

Reconfigurable intelligent surfaces (RIS) can actively perform beamforming and have become a crucial enabler for the wireless systems in the future. The direction-of-arrival (DOA) estimates of RIS received signals can help design the reflection control matrix and improve communication quality. In this paper, we design a RIS-assisted system and propose a robust Lawson norm-based multiple-signal-classification (LN-MUSIC) DOA estimation algorithm for impulsive noise environments, which is divided into two parts: first, the non-convex Lawson norm is used as the error criterion along with a regularization constraint to formulate the optimization problem. Then, a Bregman distance based alternating-direction-method-of-multipliers (ADMM) is used to solve the problem and recover the desired signal. The second part is to use the multiple-signal-classification (MUSIC) to find out the DOAs of targets based on their sparsity in the spatial domain. In addition, we also propose a RIS control matrix optimization strategy that require no channel state information (CSI), which effectively enhance the strength of desired signals and improve the performance of the devised algorithm. A Cramër-Rao-lower-bound (CRLB) of the proposed DOA estimation algorithms is presented and verifies the feasibility of the algorithm. Simulated results show that the created robust DOA estimate algorithm realized using the Lawson norm can effectively suppress the impact of large outliers caused by impulsive noise on the estimation results, outperforming existing methods.