提出自适应频跳框架，对抗预测式干扰并控制风险。
Model-Adaptive and Risk-Constrained Frequency Hopping Against Predictive Jammers
- 用动态模型选择机制应对不同通信环境下的干扰变化
- 在可观察切换下恢复95.5%局部最优收益，负控下减少77.7%性能下降
- 支持风险约束与实时安全调整，适合高可靠性通信系统
针对预测式干扰下的自适应频跳问题，需同时应对模型不确定性与策略暴露：上下文丢失关系在不同工作模式中可能变化，持续的跳频模式可能使高概率信道暴露于攻击。我们提出D-PACT-AFH，一种模型自适应且风险受限的对抗性上下文-赌博框架，其中Tsallis-FTRL主控制器结合全局线性学习器与分段本地学习器，并在线选择模型类别。D-PACT-Hit将信道级边际命中风险引入模型选择，D-PACT-Safe通过最小Kullback-Leibler投影实现每时隙风险预算约束。理论证明在非预知攻击下估计器有效性、相对于更优固定基的基准分解，以及对安全投影的精确条件风险保证。跨多种信道环境与干扰类型实验表明，该方法实现有效模型自适应与可控吞吐-风险权衡：在可观测切换场景下，恢复本地学习器95.5%收益；在负控场景下避免77.7%性能退化。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。