Location Prediction Using Bayesian Optimization LSTM for RIS-Assisted Wireless Communications

Reconfigurable intelligent surface (RIS) represent a novel form of electromagnetic metamaterial that have been extensively studied for user equipment (UE) positioning by exploiting the multipath propagation of signals. A novel RIS-assisted localization prediction (RLP) method based on Bayesian optimization and long short-term memory (BO-LSTM) has been proposed in this paper. This method capitalizes on the predictive advantages of LSTM for data sequence and RIS's flexible and controllable multidimensional feature parameters, establishing a mobile UE localization model in an RIS-assisted wireless communications system based on the interplay between time slot transmission power and user location information. In order to provide a more stable communication environment for data collection during the localization process, a power allocation optimization (PAO) method is proposed for maximizing time slot channel capacity in the RLP system based on the number of RIS reflection elements. The study conducts a thorough comparison of simulation results of BO-LSTM, convolutional neural networks (CNN)-LSTM and improved bidirectional LSTM (BiLSTM) combined with Adaptive boost, employing adaptive moment estimation (Adam) and stochastic gradient descent with momentum (SGDM) optimizers. Experimental results demonstrate that the BO-LSTM-based RLP method exhibits improved prediction accuracy. These findings suggest the effectiveness of the proposed method and highlight its potential for further enhancement.