发表机构
School of Electrical and Electronic Engineering, Nanyang Technological University; Università di Udine（南洋理工大学电气与电子工程学院; 乌迪内大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出D-PACT-AFH框架，结合Tsallis-FTRL与全局/局部学习器，通过风险约束投影应对预测性干扰，实现模型自适应并权衡吞吐与风险。
AI 中文摘要
针对预测性干扰的自适应跳频必须同时处理模型不确定性和策略暴露问题：上下文-损失关系可能在不同运行场景下发生变化，而持续的跳频模式可能将高概率信道暴露给攻击。我们提出D-PACT-AFH，一个模型自适应且风险约束的对抗性上下文老虎机框架，其中Tsallis-FTRL主算法结合全局线性学习器与分区局部学习器，并在线选择模型类别。D-PACT-Hit将信道级边际命中风险纳入模型选择，而D-PACT-Safe应用最小KL散度投影以强制执行每时隙风险预算。我们建立了在非预期攻击下的估计量有效性、相对于更优固定基线的预言机分解，以及Safe投影的精确条件风险保证。跨多种信道场景和干扰机类型的实验证明了有效的模型自适应和可控的好put-风险权衡：D-PACT-AFH在可观察切换下恢复了局部学习器95.5%的增益，同时在阴性对照场景中避免了其77.7%的性能退化。
英文摘要
Adaptive frequency hopping against predictive jamming must address both model uncertainty and policy exposure: the context-loss relationship may vary across operating regimes, while persistent hopping patterns may expose high-probability channels to attack. We propose D-PACT-AFH, a model-adaptive and risk-constrained adversarial contextual-bandit framework in which a Tsallis-FTRL master combines a global linear learner with a partitioned local learner and selects the model class online. D-PACT-Hit incorporates channel-wise marginal hit risk into model selection, while D-PACT-Safe applies a minimum-Kullback-Leibler projection to enforce a per-slot risk budget. We establish estimator validity under non-anticipating attacks, an oracle decomposition relative to the better fixed base, and an exact conditional-risk guarantee for the Safe projection. Experiments across diverse channel regimes and jammer types demonstrate effective model adaptation and a controllable goodput-risk tradeoff: D-PACT-AFH recovers 95.5% of the local learner's gain under observable switching while avoiding 77.7% of its degradation in a negative-control regime.
