A Modern RIS Design Based AI Driven for Cooperative Relay Networks

Abstract

This study addresses beam squint mitigation in millimeter-Wave (mmWave) systems using a 1024- element Reconfigurable Intelligent Surface (RIS) optimized via gradient descent. The proposed approach achieves ±0.2° squint correction and over 90% accuracy within 200 iterations, with gain variation maintained below 0.5 dB. A supervised Machine Learning (ML) model, trained on simulation data, demonstrates an 83% reduction in training error and a 60% drop in validation error over 1000 epochs, converging by epoch 700. The integration of early stopping and L2 regularization is suggested to further reduce generalization error. These results indicate that RIS, combined with ML optimization, offers a scalable and effective solution for wideband mmWave systems, paving the way for real-time beamforming in next-generation wireless networks.