Machine Learning-Based Strategies for Intelligent Reflecting Surface Configuration, Network Optimization, and Security Enhancement

Abstract

This research  presents a comprehensive investigation of machine learning-based strategies designed to optimize intelligent reflecting surface (IRS) configurations, enhance network performance, and fortify security protocols in next-generation wireless communication systems. The proposed framework integrates advanced neural network models, robust optimization techniques, and adaptive signal processing methods to dynamically configure IRS elements and mitigate interference in complex propagation environments. Our approach leverages multi-dimensional channel state information and mathematical constructs including vector spaces, matrix decompositions, and probabilistic models to systematically derive optimal reflective parameters. Key contributions include the formulation of a novel optimization problem that encapsulates the interplay between IRS phase adjustments and network throughput, and the development of a gradient-based algorithm for rapid convergence. Detailed theoretical analyses supported by rigorous simulation results validate the proposed scheme, highlighting significant improvements in signal-to-noise ratio, spectral efficiency, and resilience against adversarial attacks. In addition, the study integrates cryptographic security measures with machine learning classifiers to detect and counteract potential vulnerabilities, thereby ensuring data integrity and confidentiality. The results underscore the potential of combining data-driven techniques with traditional signal processing to address the challenges of high-dimensional wireless channels and emerging security threats. Our work provides valuable insights into the design of adaptive, secure, and efficient wireless networks, paving the way for future developments in intelligent communication systems and related applications.