ISAC Multi-Active STAR-RIS-aided NOMA System with Element Selection: A DRL Approach

In this paper, to obtain high efficiency and performance in a wireless communication system, a multi-active Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-aided Integrated Sensing and Communication (ISAC) framework utilizing Non-Orthogonal Multiple Access (NOMA) technology is introduced. Unlike conventional STAR-RIS systems that employ either fully passive or fully active elements, the element selection approach allows each element to operate in either an active or passive mode to reduce power consumption. We aim to maximize the energy efficiency (EE) of the system by collaboratively optimizing the base station (BS) transmit beamforming vectors, the STAR-RIS coefficient matrices, and the element selection matrix. The formulated optimization problem is non-convex, which makes it challenging to solve using conventional methods. To tackle this challenge, we propose a deep reinforcement learning (DRL) approach to optimize the active-passive mode vector, phase shifts, and BS beamforming. Consequently, a twin delayed deep deterministic policy gradient (TD3) algorithm is applied to address the complex optimization problem. Simulation results reveal that our proposed method achieves a higher EE than current benchmarks.