Integrated Sensing and Communication for Symbiotic Radio With Multiple IoT Devices

The integration of sensing and communication (ISAC) in symbiotic radio (SR) systems enables highly efficient simultaneous localization and information transmission for both primary and secondary systems. However, implementing ISAC for SR with multiple IoT Devices (IoTDs) poses significant challenges. Firstly, achieving two-dimensional (2D) direction-of-arrival (DoA) sensing escalates the complexity of compressed sensing algorithms due to the large dimension dictionary matrix. Additionally, obtaining channel state information (CSI) for secondary systems with multiple IoTDs introduces significant overhead, necessitating efficient communication strategies using partial CSI. To tackle these challenges, this letter introduces the decoupled 1D off-grid sparse Bayesian learning (De-1D-OffSBL)-based ISAC algorithm. Specifically, for low-complexity multi-target sensing, we propose decoupling the 2D DoA sensing problem into two independent 1D DoA estimation problems. Furthermore, by leveraging partial CSI and sensing information, we propose a multi-tag SR symbol detection method based on a carefully designed modulation scheme. Numerical results are provided to validate the effectiveness and superiority of the proposed De-1D-OffSBL ISAC algorithm.