Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize throughput in RF-powered cognitive radio networks, enabling autonomous decision-making in complex environments.

Contribution

It introduces a novel DQN-based method that outperforms existing techniques in large-scale RF-CRN throughput maximization.

Findings

Performance speedup of up to 1.8x over advanced DQN techniques

Effective autonomous decision-making in complex RF-CRN environments

Demonstrated improved throughput optimization in large-scale networks

Abstract

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.