Transactions of Nanjing University of Aeronautics & Astronautics
Learning to Survive Jamming: A Survey of AI-Driven Physical-Layer Anti-jamming in Wireless Communications
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Abstract:
The broadcast nature of wireless channels exposes communication links to intentional jamming， which is a threat that will be exacerbated in 6G networks owing to dense spectrum reuse， massive connectivity， and space-air-ground integration. Conventional physical-layer （PHY） countermeasures rely on accurate models and approximately stationary jammer statistics， and their effectiveness degrades under adaptive and rapidly varying jamming. Artificial intelligence （AI） relaxes these assumptions by learning detection rules and adaptation policies directly from signal， spectrum， and interaction data. This survey provides a comprehensive review of AI-driven PHY anti-jamming techniques， organized through a unified dual-axis taxonomy of learning paradigms and PHY functions. We first present a unified jamming model and formulate the associated learning problem under distribution shift， implementation constraints， and adversarial uncertainty. Six paradigm groups are then examined： Supervised deep learning； deep reinforcement learning； transfer， meta， and continual learning； federated and distributed learning； generative and self-supervised learning； and end-to-end learning with deep unfolding. Existing work is subsequently organized by five PHY functions： jamming sensing， frequency-domain adaptation， spatial-domain suppression， link adaptation， and cross-domain joint optimization. Finally， we discuss critical deployment constraints and validation gaps， and identify research directions toward robust and standards-compliant anti-jamming solutions.
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This work was supported in part by the National Key Research and Development Program of China （No.2023YFC3305904）， in part by the National Natural Science Foundation of China （No.62301049）， and in part by the Natural Science Foundation of Shandong Province of China （No.ZR2024QF028）.
Clc Number:
TN925
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