  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be
the eyes and ears of upcoming modern networks such as Internet of Things (IoT),
requiring increased decentralization and autonomous operation. To be considered
autonomous, the RF-powered network entities need to make decisions locally to
maximize the network throughput under the uncertainty of any network
environment. However, in complex and large-scale networks, the state and action
spaces are usually large, and existing Tabular Reinforcement Learning technique
is unable to find the optimal state-action policy quickly. In this paper, deep
reinforcement learning is proposed to overcome the mentioned shortcomings and
allow a wireless gateway to derive an optimal policy to maximize network
throughput. When benchmarked against advanced DQN techniques, our proposed DQN
configuration offers performance speedup of up to 1.8x with good overall
performance.
