RIS-Enhanced Cognitive Integrated Sensing and Communication: Joint Beamforming and Spectrum Sensing

Abstract

Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks. However, their interplay has yet to be explored. To obtain the mutual benefits between CR and ISAC, we focus on a reconfigurable intelligent surface (RIS)-enhanced cognitive ISAC system and explore using the additional degrees-of-freedom (DoFs) brought by the RIS to improve the performance of the cognitive ISAC system. Specifically, we mathematically prove that the position error bound (PEB) of each mobile sensor (MS) decreases with the increasing signal-to-noise ratio (SNR) of the received signals at each MS. We also formulate an optimization problem of maximizing the signal-to-noise-plus-interference ratios (SINRs) of the MSs while ensuring the requirements of the spectrum sensing (SS) and the secondary transmissions by jointly designing the SS time, the secondary base station (SBS) beamforming, and the RIS beamforming. The formulated non-convex problem can be solved by the proposed block coordinate descent (BCD) algorithm based on the Dinkelbach’s transform and the successive convex approximation (SCA) methods. Simulation results demonstrate that all the proposed iterative algorithms converge fast, and the SINRs of MSs can be effectively enhanced by increasing the transmit power of the SBS, the number of MS antennas, and the number of RIS elements. Moreover, higher MS SINRs lead to lower PEBs of MSs, thereby having the potential to improve the accuracy of radio environment map (REM) for CR networks. Additionally, the RIS needs to be deployed near the SBS or MSs to guarantee the performance gain brought by the RIS.