Robust Beamforming for RIS Enhanced Transmissions in Cognitive Radio Networks
Bai Zhao, Min Lin, Shengjie Xiao, Ming Cheng, Wei‐Ping Zhu, Naofal Al‐Dhahir
Abstract
We propose a robust beamforming (BF) scheme for reconfigurable intelligent surface (RIS) enhanced transmission to support heterogeneous services with diverse signal-to-interference-plus-noise ratio requirements in cognitive radio networks (CRNs). Here, the CRN coexisting with a primary network offers connection-centric services and content-aware services through space division multiple access and RIS-aided multicast technology, respectively. Using imperfect statistical channel state information, the RIS enhanced transmission scheme is formulated as a non-convex optimization problem with outage constraints. To address this intractable problem, we first use the cumulative distribution function of a standard normal distribution and Schur complement approaches to transform the non-convex outage constraints into solvable ones. Then, a robust BF algorithm integrating alternate optimization with semidefinite relaxation methods is proposed to obtain the active BF weight vectors at the cognitive base station and the phase shift matrix at the RIS. Our simulation results demonstrate the robustness of the proposed BF algorithm and the superiority of the RIS enhanced wireless transmission.