Statistical‐ CSI ‐Aware Secrecy Energy Efficiency Maximization in Dual‐ RIS Networks With Mobile Eavesdropper

ABSTRACT

In this paper, the secrecy energy efficiency (SEE) maximization problem is investigated in a dual‐reconfigurable intelligent surface (RIS)‐assisted downlink multiple‐input single‐output (MISO) system with an intermittently active mobile eavesdropper under statistical channel state information (CSI). The proposed framework models the eavesdropper channel using a statistical mean‐covariance representation, rather than conventional approaches that assume instantaneous eavesdropper CSI. A deterministic secrecy‐rate surrogate is obtained based on the expected eavesdropper signal‐to‐interference‐plus‐noise ratio (SINR) and an uncertainty‐aware robustness penalty. The resulting SEE optimization problem is nonconvex due to the fractional objective function, coupled beamforming and RIS phase variables, and unit‐modulus RIS constraints. To address this issue, a low‐complexity alternating optimization algorithm is designed using Dinkelbach fractional programming. The results demonstrate that the proposed dual‐RIS statistical‐CSI framework achieves 9.62 Mbit/J SEE at 30 dBm transmit power, outperforming the single‐RIS statistical‐CSI benchmark while approaching the perfect‐CSI dual‐RIS benchmark. Overhead‐aware evaluation further confirms the effectiveness of the proposed dual‐RIS framework under practical operating conditions.