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

arXiv: https://arxiv.org/abs/2508.08206 · HTML: https://mlsanity.com/paper/2508.08206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [Fast Convergence Rates for Distributed Non-Bayesian Learning](https://mlsanity.com/paper/1508.05161/) (2015)
- [Intelligent Surfaces Empowered Wireless Network: Recent Advances and The Road to 6G](https://mlsanity.com/paper/2312.16918/) (2023)
- [Target Sensing with Intelligent Reflecting Surface: Architecture and Performance](https://mlsanity.com/paper/2201.09091/) (2022)
- [Robust Deep Sensing Through Transfer Learning in Cognitive Radio](https://mlsanity.com/paper/1908.00658/) (2019)
- [Spectral and Energy Efficiency of IRS-Assisted MISO Communication with Hardware Impairments](https://mlsanity.com/paper/2004.09854/) (2020)
- [Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems](https://mlsanity.com/paper/2109.13058/) (2021)
- [Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements](https://mlsanity.com/paper/2206.06605/) (2022)
- [Performance Analysis of Intelligent Reflecting Surface Assisted Opportunistic Communications](https://mlsanity.com/paper/2203.06313/) (2022)
- [Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information](https://mlsanity.com/paper/2302.09717/) (2023)
- [Deep Learning Based Joint Beamforming Design in IRS-Assisted Secure Communications](https://mlsanity.com/paper/2304.01702/) (2023)
- [Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided Systems](https://mlsanity.com/paper/2403.01093/) (2024)
- [Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA](https://mlsanity.com/paper/2406.03391/) (2024)

## Abstract (arXiv)

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.