Self-Sustainable Multi-Functional RIS-Enabled Integrated Sensing and Communication Systems

Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) systems enhance spectrum efficiency and sensing accuracy. Building on this, we propose a novel self-sustainable multi-functional RIS (S-MFRIS) concept that supports multiple functionalities: reflection, refraction, amplification, energy harvesting, and target sensing. By harvesting energy from incident signals, the S-MFRIS can reflect, refract, and amplify signals without needing an external power supply, effectively overcoming double-fading attenuation. Furthermore, by deploying low-cost sensor elements, the S-MFRIS can capture echo signals from multiple targets, mitigating the signal attenuation commonly associated with multi-hop links. Then, we establish an S-MFRIS-enabled ISAC system and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of the sensing targets, subject to constraints on communication rate, power budget, and reflection coefficients. To solve this non-convex problem, we decompose it into three sub-problems, which are efficiently addressed using an iterative algorithm. Simulation and numerical results demonstrate the following key findings: (1) The proposed algorithm achieves better convergence and performance than the semidefinite relaxation-based and random-based algorithms. (2) The performance of the MFRIS-aided system varies under different operating protocols, with the self-sustainable MFRIS outperforming other schemes, particularly when the power budget is sufficient. (3) The proposed S-MFRIS achieves $30\%-46\%$ sensing SINR gains at most for the same total power budget or element configuration. (4) The number of sensing elements improves sensing performance up to a certain point, after which further increases in the number of elements yield diminishing returns.