  By using smart radio devices, a jammer can dynamically change its jamming
policy based on opposing security mechanisms; it can even induce the mobile
device to enter a specific communication mode and then launch the jamming
policy accordingly. On the other hand, mobile devices can exploit spread
spectrum and user mobility to address both jamming and interference. In this
paper, a two-dimensional anti-jamming mobile communication scheme is proposed
in which a mobile device leaves a heavily jammed/interfered-with frequency or
area. It is shown that, by applying reinforcement learning techniques, a mobile
device can achieve an optimal communication policy without the need to know the
jamming and interference model and the radio channel model in a dynamic game
framework. More specifically, a hotbooting deep Q-network based two-dimensional
mobile communication scheme is proposed that exploits experiences in similar
scenarios to reduce the exploration time at the beginning of the game, and
applies deep convolutional neural network and macro-action techniques to
accelerate the learning speed in dynamic situations. Several real-world
scenarios are simulated to evaluate the proposed method. These simulation
results show that our proposed scheme can improve both the
signal-to-interference-plus-noise ratio of the signals and the utility of the
mobile devices against cooperative jamming compared with benchmark schemes.
