Joint Active and Passive Beamforming for Energy-Efficient STARS with Quantization and Element Selection in ISAC Systems

Abstract: This paper investigates a simultaneously transmitting and reflecting reconfigurable intelligent surface (STARS)-aided integrated sensing and communication (ISAC) systems in support of full-space energy-efficient data transmissions and target sensing. We formulate an energy efficiency (EE) maximization problem jointly optimizing dual-functional radar-communication (DFRC)-empowered base station (BS) ISAC beamforming and STARS configurations of amplitudes, phase-shifts, quantization levels as well as element selection. Furthermore, relaxed/independent/coupled STARS are considered to examine architectural flexibility. To tackle the non-convex and mixed-integer problem, we propose a joint active-passive beamforming, quantization and element selection (AQUES) scheme based on alternating optimization: Lagrangian dual and Dinkelbach's transformation deals with fractions, whereas successive convex approximation (SCA) convexifies the problem; Penalty dual decomposition (PDD) framework and penalty-based convex-concave programming (PCCP) procedure solves amplitude and phase-shifts; Heuristic search decides the quantization level; Integer relaxation deals with the element selection. Simulation results demonstrate that STARS-ISAC with the proposed AQUES scheme significantly enhances EE while meeting communication rates and sensing quality requirements. The coupled STARS further highlights its superior EE performance over independent and relaxed STARS thanks to its reduced hardware complexity. Moreover, AQUES outperforms existing configurations and benchmark methods in the open literature across various network parameters and deployment scenarios.