  RF-powered backscatter communication is a promising new technology that can
be deployed for battery-free applications such as internet of things (IoT) and
wireless sensor networks (WSN). However, since this kind of communication is
based on the ambient RF signals and battery-free devices, they are vulnerable
to interference and jamming. In this paper, we model the interaction between
the user and a smart interferer in an ambient backscatter communication network
as a game. We design the utility functions of both the user and interferer in
which the backscattering time is taken into the account. The convexity of both
sub-game optimization problems is proved and the closed-form expression for the
equilibrium of the Stackelberg game is obtained. Due to lack of information
about the system SNR and transmission strategy of the interferer, the optimal
strategy is obtained using the Q-learning algorithm in a dynamic iterative
manner. We further introduce hotbooting Q-learning as an effective approach to
expedite the convergence of the traditional Q-learning. Simulation results show
that our approach can obtain considerable performance improvement in comparison
to random and fixed backscattering time transmission strategies and improves
the convergence speed of Q-Learning by about 31%.
