Distributed Rate Optimization For Intelligent Reflecting Surface With Federated Learning

Intelligent reflecting surface (IRS) has been proposed as a potential solution to improve the performance of many aspects for future wireless communication. However, the issue of user's privacy in IRS assisted communications is often ignored in previous works. In this paper, we propose an algorithm, namely optimal beam reflection based on federated learning (OBR-FL), to achieve high speed communication with the sparse channel state information (CSI). The corresponding configuration matrix of IRS can be determined by the trained model according to the CSI of user to achieve the optimal communication rate. Based on federated learning (FL), several local models are trained with the local dataset of each user and upload them to a central server for aggregation to generate a global model. Then, each user downloads this global model as the initial configuration for next training round. Finally, the optimal model is obtained after several iterations. During the training process, the private data of each user is managed and processed locally. Simulation results demonstrate that the achievable rate performance of the proposed algorithm can effectively approach to that of the centralized machine learning (ML) while protecting user's privacy.