Active and STAR-RIS Assisted MIMO ISAC Systems with SWIPT

As an innovative framework for sustainable communication within next-generation networks, reconfigurable intelligent surfaces (RISs) have developed potential transformation in improving simultaneous wireless information and power transfer (SWIPT). Alongside SWIPT, integrated sensing and communication (ISAC) has gained significant attention for its ability to combine communication and sensing functionalities within the same infrastructure, optimizing resource utilization and improving system performance. In this paper, we propose a framework that integrates an active (A-RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) assisted multiple-input multiple-output (MIMO) technology for SWIPT in an ISAC system. The system consists of an A-RIS, a STAR-RIS, a dual functional base station (DFBS), a group of reflection and transmission communication users (CUs) and reflection and transmission energy receiving devices (EDs), sensing targets simultaneously with the aid of RIS. We formulate an optimization problem to maximize the target rate while balancing the communication rate under energy harvesting (EH) constraints, and phase-shits constraints. It implies the trade-offs between target sensing accuracy and communication performance, demonstrating the potential of RIS to enhance system capabilities in diverse ISAC scenarios. The optimization problem and its constraints are inherently non-convex due to the high coupling between variables. Consequently, we employ the minimum mean square error method to address the non-convex nature of the problem. Thereby, it simplifies the problem by transforming it into a more manageable form and then applying an alternating optimization framework. This framework addresses the design of beamforming challenges at both the DFBS and the RIS (A-RIS/STAR-RIS) separately, by solving them iteratively using general approximation techniques. The analysis highlights the advantages of the proposed ARIS and STAR-RIS assisted SWIPT for ISAC framework over conventional RIS by achieving performance gain of 12-15%.