1 research outputs found
UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming
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