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Life sciences · Preprint
arXiv · September 6, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes D-PACT-AFH, a machine-learning-based frequency hopping framework designed to adapt to predictive jamming while managing risk exposure. The work is theoretical and simulation-based; it has not undergone peer review and addresses a signal-processing problem rather than a clinical question.
Preprint. Intervention: D-PACT-AFH framework: model-adaptive frequency hopping combining Tsallis-FTRL master with global linear and partitioned local learners; two variants (D-PACT-Hit and D-PACT-Safe) incorporate risk constraints.. Compared with: Local learner baseline under observable switching and negative-control regime.
D-PACT-AFH recovers 95.5% of the local learner's gain under observable switching D-PACT-AFH avoids 77.7% of local learner's degradation in negative-control regime Framework establishes oracle decomposition and exact conditional-risk guarantee for Safe projection variant
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a preprint in computer science/signal processing describing a novel algorithmic framework for adaptive frequency hopping; it has not been peer reviewed and is not a clinical study.
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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.
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