Intelligent Anti-Jamming Based on Deep-Reinforcement Learning and Transfer Learning

One of the security issues in a wireless network is jamming attacks, where the jammer causes congestion and significant decrement in the network throughput by obstructing channels and disrupting user signals. In this thesis, we first develop a deep reinforcement learning (DRL) model to confront the jammer. However, training a DRL model from scratch may take a long time. We further propose a transfer learning (TL) approach to enable the DRL agent to learn fast in dynamic wireless networks to confront jamming attacks effectively. To make our proposed TL method adaptive to different network environments, we propose a novel method to quantitatively measure the difference between the source and target domains, using an integrated feature extractor. Afterward, based on the measured difference, we demonstrate how it can help choosing an efficient setting for the TL model leading to a fast and energy-efficient learning. We also show that the proposed TL method can effectively reduce the training time for the DRL model and outperforms other existing TL methods.