Sum Rate Optimization via Joint Beamforming in RIS-Assisted ISAC System: A Manifold-Based Approach
Article
Samir, A, Ibrahim, AS, Ismail, MH et al. (2026). Sum Rate Optimization via Joint Beamforming in RIS-Assisted ISAC System: A Manifold-Based Approach
. IEEE Open Journal of the Communications Society, 7 786-801. 10.1109/OJCOMS.2025.3649379
Samir, A, Ibrahim, AS, Ismail, MH et al. (2026). Sum Rate Optimization via Joint Beamforming in RIS-Assisted ISAC System: A Manifold-Based Approach
. IEEE Open Journal of the Communications Society, 7 786-801. 10.1109/OJCOMS.2025.3649379
Samir, Ahmed; Ibrahim, Ahmed S; Ismail, Mahmoud H; Elsayed, Mohamed
abstract
Integrated sensing and communication (ISAC) systems have emerged as a key technology for next-generation wireless networks. ISAC systems face a challenge due to the differing priorities between communication and sensing metrics: communication focuses on maximizing data rates, while sensing prioritizes accurate target detection. To address this, reconfigurable intelligent surfaces (RIS) are introduced to enhance beamforming control, thereby improving both communication and sensing performance. This paper presents an optimization framework for an RIS-assisted ISAC system, aiming to maximize the communication sum rate (SR) while maintaining an acceptable threshold for the sensing metric, specifically the signal-to-clutter-noise ratio (SCNR). The proposed framework utilizes a low-complexity Riemannian manifold optimization technique to jointly design the transmit and receive beamformers at the ISAC base station (BS) and the RIS phase shifts, following an alternating optimization (AO) approach. Specifically, the beamforming and RIS phase shifts are optimized over the complex cone and complex circle manifolds, respectively. To demonstrate the effectiveness of this manifold-based approach, the same problem is also solved using AO with classical semidefinite programming (SDP) optimization. Simulation results show that the proposed manifold-based algorithm outperforms existing methods in both communication and sensing metrics, while also reducing computational complexity.
