Beamforming Design for RIS-Aided Integrated Sensing, Communication, and Computation Systems

The deployment of various emerging wireless services has necessitated that sixth-generation (6G) wireless networks integrate sensing, computing and communication within the same radio spectrum. However, it is challenging to improve the performance of the integrated sensing, communication, and computation (ISCC) systems in complex propagation environment. In this paper, we investigate the beamforming design problem in reconfigurable intelligent surfaces (RIS)-aided ISCC systems, where an RIS can provide additional spatial degree of freedom. To this end, we first formulate a joint beamforming problem for RIS-aided ISCC, aiming to concurrently improve the performance for multiple target sensing, multi-dimensional computation, and multi-stream communication. Then we propose two joint beamforming algorithms: one is weight performance optimization (WPO) algorithm that minimizes total mean square error (MSE) of various demands under power constraints, while the other one is latency minimization and power management (LMPM) algorithm that reduces total power consumption and transmission latency, keeping the sensing MSE within a specified limit. In addition, we analyze the impact of key system parameters on the overall performance of ISCC, providing valuable guidelines for practical system design. Finally, the performance of the proposed algorithm is validated by the simulation, which shows that the sensing error of WPO algorithm is reduced by at least 46.7% compared with the existing popular algorithms, while the computation rate and communication rate are increased by more than 49.1% and 22.2%, respectively. Additionally, The total transmission power is reduced by 62.1%-92.9% by LMPM.