Abstract:
Integrated sensing and communication (ISAC) is regarded as a promising approach
to alleviate spectrum congestion in future communication networks. Leveraging recent
breakthroughs in programmable meta-materials, re-configurable intelligent surfaces
(RIS) enhance communication systems by enabling multiple input multiple output
(MIMO) transmission without requiring additional radio frequency (RF) chains.
Therefore, integrating RIS into ISAC systems presents a promising solution to address
the fundamental challenge of enhancing ISAC system performance through only active
beamforming, especially in adverse channel conditions. This Study investigates the
joint optimization of transmit beamforming and RIS reflection coefficients to maximize
the sum capacity of ISAC systems. The study adopts a more practical approach
by constraining each RIS element to use only a discrete set of phase shifts. Most contemporary
works have used mathematical optimization tools to address the resulting
non-convex optimization problem, leading to sub-optimal solutions. Thus, we address
the non-convex optimization problem using the twin delayed deep deterministic (TD3)
deep reinforcement learning (DRL) algorithm. Numerical results and analysis demonstrate
improved sum capacity of the ISAC system using the suggested approach.
