  In an RF-powered backscatter cognitive radio network, multiple secondary
users communicate with a secondary gateway by backscattering or harvesting
energy and actively transmitting their data depending on the primary channel
state. To coordinate the transmission of multiple secondary transmitters, the
secondary gateway needs to schedule the backscattering time, energy harvesting
time, and transmission time among them. However, under the dynamics of the
primary channel and the uncertainty of the energy state of the secondary
transmitters, it is challenging for the gateway to find a time scheduling
mechanism which maximizes the total throughput. In this paper, we propose to
use the deep reinforcement learning algorithm to derive an optimal time
scheduling policy for the gateway. Specifically, to deal with the problem with
large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that
enables the gateway to learn the optimal policy. The simulation results clearly
show that the proposed deep reinforcement learning algorithm outperforms
non-learning schemes in terms of network throughput.
