Uplink–Downlink Resource Optimization for STAR-RIS-Assisted ISCC Networks With NOMA

To address the waste of spectrum resources caused by the separate operation of integrated sensing and communication (ISAC) and mobile edge computing (MEC), the integrated sensing, communication, and computing (ISCC) paradigm has been proposed. However ISCC systems are still facing serious channel fading and obstacle occlusion problems, which are expected to be solved by the emerging simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In this paper, we propose a STAR-RIS-assisted ISCC framework, where the computational tasks from users are offloaded to a base station (BS) for processing with the assistance of a STAR-RIS, and the results are subsequently downloaded back to the corresponding users. Notably, target sensing operation is executed concurrently throughout the process of result downloading. To enhance communication efficiency, the non-orthogonal multiple access (NOMA) scheme is incorporated. The number of offloading bits of users is maximized by optimizing the uplink-downlink resource of the network, including the received beamforming vector, active beamforming vector, time slot, phase shift of STAR-RIS, and computational resource. In order to solve the highly complex non-convex problem, we employ the block coordinate descent (BCD) framework to decompose the proposed problem into three subproblems. The convex-concave procedure (CCCP) method is used to deal with the nonconvex constraints. For the rank-1 constraints, the semidefinite relaxation (SDR) approach and the penalty method are invoked. The numerical results show that: 1) the application of STAR-RIS significantly improves the offloading capability of the network, 2) the NOMA scheme significantly outperforms the orthogonal multiple access (OMA) scheme, and 3) comparing with other benchmark schemes, the proposed algorithm significantly improves the overall network performance.