Few-Shot Intelligent Anti-Jamming Access with Fast Convergence: A GAN-Enhanced Deep Reinforcement Learning Approach

Few-Shot Intelligent Anti-Jamming Access with Fast Convergence: A GAN-Enhanced Deep Reinforcement Learning Approach

To address the small-sample training bottleneck and inadequate convergence efficiency of Deep Reinforcement Learning (DRL)-based communication anti-jamming methods in complex electromagnetic environments, this paper proposes a Generative Adversarial Network-enhanced Deep Q-Network (GA-DQN) anti-jamm...

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Bibliographic Details
Main Authors: Tianxiao Wang, Yingtao Niu, Zhanyang Zhou
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Applied Sciences
Subjects:
wireless communication
anti-jamming
generative adversarial network
deep reinforcement learning
Deep Q-Network
Online Access:https://www.mdpi.com/2076-3417/15/15/8654
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