Randomized Passive Energy Beamforming for Cooperative Localization in Reconfigurable Intelligent Surface-Assisted Wireless Backscattered Sensor Network

Localization is essential for network management of the wireless backscattered sensor networks (WBSNs). In large-scale WBSN, the cooperative localization among the passive nodes effectively improves the localization accuracy. Meanwhile, reconfigurable intelligent surface (RIS) motivates nodes to gain better spatial channel using passive beamforming or phase modulation. In this article, we analyze the impact of passive beamforming for RIS on the localization accuracy of cooperative localization in the WBSN system. We derive the Fisher information matrix (FIM) and the spatial position error bound for the fully connected communication network system. We demonstrate that the phase modulation of RIS reflection units affect the localization accuracy of the cooperative localization WBSN system. However, RIS passive beamforming as a discrete and nonconvex integer programming problem is difficult to solve. Then, we propose a Monte Carlo-based random RIS passive beamforming to achieve the maximum localization accuracy. We apply Gibbs sampling and resampling methods to generate the phase shift vector samples of RIS. The sample with the highest localization accuracy is considered as the optimal solution. The simulation results demonstrate that our proposed method for RIS passive beamforming can improve 34.5% localization accuracy in the Line-of-Sight (LoS) case, while the genetic algorithm (GA) is 6.8%. In the Non-LoS (NLoS) environment, the localization accuracy improvement of our proposed method reaches 97%, and GA can only reach 85% as the comparison.