Gridding Based Reconfigurable Intelligent Surface-aided Wireless Network Optimization

Aiming at the problem of weak coverage resulting from interference and obstructions in wireless networks, we propose a gridding based Reconfigurable Intelligent Surface (RIS)-assisted optimization scheme that divides problem area and RIS deployment area into several grids for better performance and lower complexity. The objective is to maximize the signal strength received in weak coverage area, with optimization variables comprising location, angle of incidence and angle of reflection of RIS. Then Particle Swarm Optimization (PSO) and Alternating Optimization (AO) algorithms are used to solve this non-convex problem. Simulation results show that our proposed scheme can achieve more than 25% improvement in terms of average SNR than conventional ones when the number of reflection elements of RIS is more than 100. Moreover, we analyze the impact of different sizes of RIS on its optimal location and the average SNR received in the weak coverage area, finding out when RIS is closer to the weak coverage area, the performance is not always better, which is different to existed observations.