Cognitive Radio Network Throughput Maximization With Deep Reinforcement Learning

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.

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keyong2020cognitive

Related papers

• Deep Reinforcement Learning For Distributed And Uncoordinated Cognitive Radios Resource Allocation (2022)0.00
• Deep Reinforcement Learning For Distributed Uncoordinated Cognitive Radios Resource Allocation (2019)0.00
• Implications Of Decentralized Q-learning Resource Allocation In Wireless Networks (2017)0.00
• Practical Policy Distillation For Reinforcement Learning In Radio Access Networks (2025)0.00
• Meta-reinforcement Learning For Fast And Data-efficient Spectrum Allocation In Dynamic Wireless Networks (2025)0.00
• Resource Management In Wireless Networks Via Multi-agent Deep Reinforcement Learning (2020)16.43
• The Cost Of Learning: Efficiency Vs. Efficacy Of Learning-based RRM For 6G (2022)0.00
• Generalization In Reinforcement Learning For Radio Access Networks (2025)0.00