AI 中文总结
研究多小区大规模 MIMO 中基于深度学习的功率控制，针对其易受攻击问题，提出形式化验证框架，用超矩形约束对手、DeepPoly 技术及约束程序分析，评估鲁棒性，训练良好模型在位置扰动±1m 时可保证局部鲁棒性并保持 1%最优性差距。
AI 中文摘要
深度学习是通过简化对近似最优解的搜索来优化无线通信的一种有前景的方法。先前基于深度学习的无线通信优化研究探索了监督学习方法，将原始用户信息映射到最优功率分配向量。虽性能良好，但易受输入扰动的对抗攻击，当前防御机制多依赖经验方法，缺乏形式化鲁棒性保证。本文提出形式化验证框架，评估多小区大规模多输入多输出（MIMO）系统中基于深度学习的功率分配对多种潜在对抗输入操作的鲁棒性。这是首次在具有非线性输出约束的回归设置中对深度神经网络进行形式化验证。通过超矩形约束对手扰动能力，采用基于抽象的边界传播技术（DeepPoly）界定潜在分配功率区间，将最低性能要求表述为约束程序进行数值可行性分析。在多小区大规模 MIMO 功率分配公开数据集上的评估表明，训练良好的模型在位置扰动±1m 时可保证局部鲁棒性，同时保持最大 1%的最优性差距。
英文摘要
Deep learning is a promising approach to optimize wireless communication by simplifying the search for near-optimal solutions. Prior studies on deep learning-based wireless communication optimization have explored supervised learning approaches that map raw user information, such as location or channel state information, to optimal power allocation vectors. While this approach demonstrates competitive performance, it is susceptible to adversarial attacks via input perturbations. Current defense mechanisms primarily rely on empirical methods, which do not provide formal guarantees of robustness. We fill this gap by proposing a formal verification framework to evaluate the robustness of deep learning-based power allocation in multi-cell massive multiple-input multiple-output (MIMO) systems against a wide range of potential adversarial input manipulations. To the best of our knowledge, this is the first attempt to formally verify deep neural networks in a regression setting with non-linear output constraints. We model the adversary's capabilities using hyper-rectangle constraints on their perturbation, adopt the abstraction-based bound-propagation technique (DeepPoly) to bound the interval of potential allocated powers, and formulate the minimum performance requirements as a constrained program for numerical feasibility analysis. Evaluation on publicly available datasets for power allocation in multi-cell massive MIMO indicates that a well-trained model can guarantee the local robustness under location perturbation by +-1m while retaining a maximum 1% optimality gap.
Commentsaccepted at VTC Fall 2026
