Cellular systems are vulnerable to jamming attacks, especially smart jammers
that choose their jamming policies such as the jamming channel frequencies and
power based on the ongoing communication policies and network states. In this
article, we present an unmanned aerial vehicle (UAV) aided cellular
communication framework against jamming. In this scheme, UAVs use reinforcement
learning methods to choose the relay policy for mobile users in cellular
systems, if the serving base station is heavily jammed. More specifically, we
propose a deep reinforcement learning based UAV relay scheme to help cellular
systems resist smart jamming without being aware of the jamming model and the
network model in the dynamic game based on the previous anti-jamming relay
experiences and the observed current network status. This scheme can achieve
the optimal performance after enough interactions with the jammer. Simulation
results show that this scheme can reduce the bit error rate of the messages and
save energy for the cellular system compared with the existing scheme.

本文提出了一种基于深度强化学习的无人机中继框架，旨在通过在动态博弈中使用以前的反干扰中继体验和观察到的当前网络状态来帮助移动用户抵御智能干扰，从而降低丢包率，提高能源利用效率。