STAR-RIS Empowered Opportunistic Cognitive Radio Networks

Cognitive radio (CR) has been identified as a highly promising spectrum-sharing technology for enhancing the spectrum efficiency of future wireless networks. Recently, the emerging reconfigurable intelligent surface (RIS) has been integrated into CR networks to further advance spectrum efficiency. However, existing works primarily focus on conventional RIS architectures, which can only adjust the reflection of incident wireless signals on one side of the RIS. As a result, these architectures cannot tune the wireless signals to improve performance across the full 360∘ coverage. Additionally, most existing studies concentrate on enhancing the transmission of the secondary user (SU), with the potential benefits to the transmission of the primary user (PU) remaining unclear. In this paper, we investigate a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-empowered opportunistic CR network, where the STAR-RIS can tune the wireless signals on both sides simultaneously. To unleash the potential of this system, we study the reflection and transmission coefficients (RTCs) optimization problem to maximize the SU’s average achievable rate while ensuring the target sensing performance and meeting the PU’s rate requirement. We then introduce a two-stage RTC optimization framework, which decouples the optimization process into sensing and transmission stages. Specifically, the RTCs in the sensing stage are optimized to improve the PU’s transmission while achieving the desired detection and false alarm probabilities. In the transmission stage, the RTCs are optimized to maximize the SU’s average achievable rate while ensuring that the PU’s rate requirements are satisfied. Furthermore, we propose a single-stage optimization method to reduce the implementation complexity caused by frequent RTC reconfigurations. Extensive simulations demonstrate the performance improvements enabled by STAR-RIS and validate the effectiveness of the proposed RTC optimization methods.