Multi-RIS-Aided Opportunistic Communication: Low-Complexity RIS Adaptive Selection and Training Method

Multi-reconfigurable intelligent surfaces (RIS) has recently gained significant interest as emerging technology for exploiting Intelligent electromagnetic environment. Inspired by opportunistic communications, a low-complexity adaptive selection and training method for multi-RISs is proposed in this paper. Firstly appropriate number of the multi-RISs is selected to assist communication. The elements of the selected RIS are grouped, and the grouped elements share a common coefficient to reduce training overhead. Secondly, the communication performance is evaluated and other RIS will be selected to assist communication if the communication performance not meet user requirement. In this way, the system performance and the training complexity of multi-RISs can be trade-off efficiently. Simulation results show that the proposed scheme outperforms the state-of-the-art benchmarks in terms of training overhead and robustness in different channel condition, and the training overhead is 30% lower compared to the centralized deployment scheme proposed in [13].