mlsanityData to 14 Oct 2026

Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction

arXiv 2502.04963 ↗First posted 7 Feb 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

This paper investigates the anti-jamming channel access problem in complex and unknown jamming environments, where the jammer could dynamically adjust its strategies to target different channels. Traditional channel hopping anti-jamming approaches using fixed patterns are ineffective against such dynamic jamming attacks. Although the emerging deep reinforcement learning (DRL) based dynamic channel access approach could achieve the Nash equilibrium (NE) under fast-changing jamming attacks, it requires extensive training episodes. To address this issue, we propose a fast adaptive anti-jamming channel access approach guided by the intuition of ``learning faster than the jammer", where a synchronously updated coarse-grained spectrum prediction serves as an auxiliary task for the deep Q network (DQN) based anti-jamming model. This helps the model identify a superior Q-function compared to standard DRL while significantly reducing the number of training episodes. Numerical results indicate that the proposed approach significantly accelerates the rate of convergence in model training, reducing the required training episodes by up to 70% compared to standard DRL. Additionally, it also achieves a 10% improvement in throughput over NE strategies, owing to the effective use of coarse-grained spectrum prediction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 55 references that are in the map. First posted on arXiv 7 Feb 2025.

Builds on

11 of 55 references.

References · 11

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  5. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
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  11. 2017Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learningcited by 2

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  1. 2025How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
  2. 2025DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming ↗
  3. 2026Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers
  4. 2025Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach against Moving Reactive Jammer
  5. 2024Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
  6. 2026Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection
  7. 2025Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
  8. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks