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

A Novel RIS-Aided Optimization Strategy for Semantic Communication System

Reconfigurable intelligent surfaces (RISs) are programmable metasurfaces capable of optimizing signal strength and reducing interference, serving as key components in maintaining the integrity of semantic information during transmission. This study explores the establishment of additional semantic transmission and reflection pathways by deploying RISs in different cells. An optimization strategy, maximizing mutual information (MI) for quality of experience (QoE)-aware modeling of the R-SC system (QR-SC), is proposed to enhance both semantic and communication performance. Additionally, a QoE-aware model is utilized for users to gauge semantic transmission performance. Experimental results indicate that QR-SC can elevate the performance of semantic communication while ensuring reliable transmission, highlighting the substantial potential of RIS in digital and energy simultaneous transmission.</p