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An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Author

Listed:
  • Yihang Du

    (Electronic Countermeasure Institute, National University of Defense Technology, Shushan District, Hefei 230000, China)

  • Ying Xu

    (Electronic Countermeasure Institute, National University of Defense Technology, Shushan District, Hefei 230000, China)

  • Lei Xue

    (Electronic Countermeasure Institute, National University of Defense Technology, Shushan District, Hefei 230000, China)

  • Lijia Wang

    (CTTL-Terminals of China Academy of Telecommunication Research of MIIT, Haidian District, Beijing 100191, China)

  • Fan Zhang

    (Science and Technology Research Bureau of AnHui XinHua University, Shushan District, Hefei 230000, China)

Abstract

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

Suggested Citation

  • Yihang Du & Ying Xu & Lei Xue & Lijia Wang & Fan Zhang, 2019. "An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning," Energies, MDPI, vol. 12(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2829-:d:250678
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Ashok Karmokar & Muhammad Naeem & Alagan Anpalagan & Muhammad Jaseemuddin, 2014. "Energy-Efficient Power Allocation Using Probabilistic Interference Model for OFDM-Based Green Cognitive Radio Networks," Energies, MDPI, vol. 7(4), pages 1-23, April.
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