Cooperative Sensing for STAR-RIS Aided Integrated Sensing and Communication Network

Existing monostatic integrated sensing and communication (ISAC) systems face challenges in realizing high-precision sensing due to a singular observation angle. This drives the exploration of cooperative sensing ISAC modes. However, communication and sensing heavily rely on line-of-sight (LoS) links, and the issue of obstacle blocking remains unavoidable even with collaborative efforts among multiple base stations (BSs). Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), can create virtual links in full space by dynamically adjusting its element coefficients. This effectively mitigates the pathloss caused by blockages. Thus, integrating STAR-RIS into cooperative ISAC networks holds substantial potential, which has not been widely investigated. In this study, a STAR-RIS-aided cooperative sensing framework is first proposed. To maximize the sensing signal-to-clutter-plus-noise ratio (SCNR), we jointly optimize the transmit beamforming at BS, transmission and reflection coefficients at STAR-RIS, and receive beamforming at multi-receivers. Also, the receivers selection is considered. To solve this nonconvex problem, we propose a joint optimization algorithm using alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR) based on the receivers selection mechanism, called the RS-AFS algorithm. Numerical results validate that the RS-AFS algorithm can significantly enhance the sensing SCNR.