A Two-Timescale Resource Allocation Scheme for RIS-Aided Cognitive Radio Systems
Jie Yuan, Hu Zhou, Ying‐Chang Liang · IEEE Transactions on Cognitive Communications and Networking · 2025
In recent years, reconfigurable intelligent surface (RIS) has been introduced into cognitive radio (CR) systems for signal enhancement and interference suppression. However, the existing RIS design for CR relies on the instantaneous channel state information (CSI) of all individual channels, which incurs high signal processing complexity and substantial channel estimation overhead. To overcome this challenge, in this paper, we propose a two-timescale (TTS) resource allocation scheme for RIS-aided CR, in which the long-term phase shifts at the RIS remain fixed during a frame, while the short-term transmit power at the secondary user transmitter (SU-TX) is optimized in each time slot within a frame. Based on this scheme, we formulate an optimization problem to maximize the ergodic rate of the SU subject to the average interference temperature (IT) constraint at the primary user receiver (PU-RX) and the average transmit power constraint at the SU-TX. To solve the non-convex optimization problem, we propose a two-stage algorithm. In the first stage, a constrained stochastic successive convex approximation (CSSCA) based algorithm is proposed to optimize the RIS phase shifts by exploiting the statistical channel state information (S-CSI) of both reflecting link and direct link channels. Once the phase shifts are obtained, in the second stage, the Lagrange dual-decomposition method is adopted to optimize the transmit power by using instantaneous CSI (I-CSI) of the composite channels, which represent the summation of the cascaded reflecting link and the direct link channels. Furthermore, three low-complexity algorithms are proposed for the long-term phase shift design. Simulation results have validated the effectiveness of our proposed TTS scheme with lower signal processing complexity and channel estimation overhead.