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

TL;DR

This paper presents a fast adaptive anti-jamming channel access method using deep Q learning combined with spectrum prediction, significantly reducing training time and improving throughput in dynamic jamming environments.

Contribution

It introduces a novel approach that integrates coarse-grained spectrum prediction with deep Q networks to accelerate learning and enhance anti-jamming performance.

Findings

Reduces training episodes by up to 70% compared to standard DRL.

Achieves 10% higher throughput than Nash equilibrium strategies.

Accelerates convergence in dynamic jamming scenarios.

Abstract

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.