Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdroppers

2025-08-11

AI Summary

In intelligent reflecting surface (IRS)-assisted wireless networks under channel state information (CSI) uncertainty, opportunistic communication is vulnerable to Byzantine eavesdroppers. Method: We propose a Byzantine-resilient framework for joint spectrum sensing and secure access, integrating log-domain Bayesian updating with attention-weighted consensus, trimmed aggregation, projection-based parameter updates, Gaussian process surrogate modeling, and constrained upper-confidence-bound Bayesian optimization—augmented by geometry-aware low-dimensional optimization to enhance robustness. Results: Experiments demonstrate that the framework significantly improves anomaly detection probability, reduces mean-square error for both honest users and eavesdroppers by 32.7%, suppresses eavesdropping signal power by an average of 18.4 dB, and accelerates convergence by approximately 2.1×. It establishes a verifiable, scalable paradigm for distributed secure learning under CSI uncertainty.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We propose a joint learning framework for Byzantine-resilient spectrum sensing and secure intelligent reflecting surface (IRS)--assisted opportunistic access under channel state information (CSI) uncertainty. The sensing stage performs logit-domain Bayesian updates with trimmed aggregation and attention-weighted consensus, and the base station (BS) fuses network beliefs with a conservative minimum rule, preserving detection accuracy under a bounded number of Byzantine users. Conditioned on the sensing outcome, we pose downlink design as sum mean-squared error (MSE) minimization under transmit-power and signal-leakage constraints and jointly optimize the BS precoder, IRS phase shifts, and user equalizers. With partial (or known) CSI, we develop an augmented-Lagrangian alternating algorithm with projected updates and provide provable sublinear convergence, with accelerated rates under mild local curvature. With unknown CSI, we perform constrained Bayesian optimization (BO) in a geometry-aware low-dimensional latent space using Gaussian process (GP) surrogates; we prove regret bounds for a constrained upper confidence bound (UCB) variant of the BO module, and demonstrate strong empirical performance of the implemented procedure. Simulations across diverse network conditions show higher detection probability at fixed false-alarm rate under adversarial attacks, large reductions in sum MSE for honest users, strong suppression of eavesdropper signal power, and fast convergence. The framework offers a practical path to secure opportunistic communication that adapts to CSI availability while coherently coordinating sensing and transmission through joint learning.

Problem

• Secure IRS-assisted wireless networks against Byzantine eavesdroppers
• Optimize BS precoder and IRS phase shifts under CSI uncertainty
• Achieve robust spectrum sensing and opportunistic access via joint learning

Innovation

• Joint learning for IRS-assisted secure communications
• Bayesian updates with trimmed aggregation in sensing
• Alternating algorithm with projected updates for CSI