Robust DOA Estimation and Communication Enhancement for RIS-Assisted Systems with Phase Mismatch

In RIS-assisted wireless systems, Gaussian-mixture noise and RIS phase mismatch can severely degrade both sensing accuracy and data transmission performance. To address these coupled impairments, this letter proposes a robust sequential sensing-aided communication enhancement framework. In the sensing stage, a multi-stage residual sparse regression (MSRSR) algorithm based on alternating minimization is developed. By incorporating atomic and & lscr;(1) -norm regularizations into a Lawson norm fidelity criterion, the resulting subproblems are solved using alternating direction method of multipliers (ADMM) and iterative shrinkage-thresholding algorithm (ISTA), enabling robust suppression of impulsive outliers and accurate recovery of the direction of arrival (DOA) and sparse mismatch matrix. In the subsequent communication stage, a low-complexity greedy coordinate descent (GCD) scheme exploits the estimated environmental priors to optimize 2-bit discrete RIS phase shifts for average achievable sum-rate maximization. Simulations verify that under severe hardware and electromagnetic constraints, the proposed scheme significantly outperforms benchmarks, approaches the reference bound under the considered simulation settings, and substantially enhances multi-node throughput.