Optimizing the Configuration of Intelligent Reflecting Surfaces using Deep Learning

We consider a multi-user wireless network, where a single base station intends to communicate with multiple users by means of an intelligent reflecting surface (IRS), and we propose to optimize the IRS configuration using deep learning-based methodologies. In particular, we train a regression deep neural network to predict the communication channel parameters given the IRS configuration vectors. We further re-train this base model using the data of different users in order to maximize a weighted sum-rate objective function. Simulation results demonstrate that our proposed approach is able to optimize the IRS configuration for any unseen test users given their corresponding received signal patterns.

Duke Scholars

Author Navid NaderiAlizadeh Biostatistics & Bioinformatics, Division of Integrative Geno ...