Jamming-Resilient Channel Estimation for Massive MIMO Uplink Against Correlated and Non-Stationary Attacks

This paper investigates correlated and non-stationary attacks conducted by malicious Intelligent Reflecting Surface (IRS) for uplink massive MIMO systems. The malicious IRS reflects the data sequence of legitimate users with arbitrary phase shifts, thereby generating non-stationary interference sequences correlated with the data sequences, which are used to jam the massive MIMO uplink. Theoretical analyses are conducted to demonstrate the detrimental effects of correlated and non-stationary attacks on a traditional eigenvalue decomposition (EVD)-based channel estimation method. We further propose a empirical-distribution-based channel estimation approach to combat against this type of attacks. The proposed method works by capturing desired convex hulls from received signals at the BS on the basis of its empirical distribution, no secret or random phase is needed. Both analysis and simulations verify the robustness of our proposed channel estimation approach as compared with the traditional EVD-based method.