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Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning

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Document pages: 5 pages

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

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