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A USRP-Based Testbed of Multi-agent Reinforcement Learning for Dynamic Spectrum Anti-Jamming

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Advances in Wireless Communications and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 190))

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Abstract

In this article, we develop a demonstrated multi-agent dynamic spectrum anti-jamming (MDSA) system using LabVIEW software and USRP-based soft defined radio platform. In the system, we design four subsystems, i.e., wireless transmission subsystem, wideband spectrum sensing subsystem, autonomous decision subsystem, and jamming subsystem. A multi-agent collaborative Q-learning (MACQL) algorithm is adopted in the autonomous decision subsystem to avoid the jamming and the co-channel interference between the agents. The dynamic process of the experiment is illustrated by the screenshots of the software. By showing that the data are successfully received and the performance of the MACQL algorithm is better than the sensing-based method, the MDSA system is realized and the effectiveness of the MACQL algorithm is demonstrated.

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Acknowledgements

This work was supported by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034, the National Science Foundation of China under Grant No. 61771488, No. 61671473, No. 61631020.

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Authors and Affiliations

  1. College of Communications Engineering, Army Engineering University, Nanjing, China

    Lijun Kong, Ximing Wang, Xufang Pei, Dianxiong Liu, Kailing Yao & Yuhua Xu

  2. School of Space Information, Space Engineering University, Beijing, China

    Luliang Jia

Authors
  1. Lijun Kong
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  2. Ximing Wang
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  3. Xufang Pei
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  4. Luliang Jia
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  5. Dianxiong Liu
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  6. Kailing Yao
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  7. Yuhua Xu
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Corresponding author

Correspondence to Lijun Kong .

Editor information

Editors and Affiliations

  1. Department of Radiocommunications and Videotechnologies, Technical University, Sofia, Bulgaria

    Roumen Kountchev

  2. Department of Computer Science, University of Auckland, Auckland, New Zealand

    Aniket Mahanti

  3. College of Information Science and Technology, Hainan University, Haikou, China

    Shen Chong

  4. School of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  5. University of Science And Technology, Moscow, Russia

    Margarita Favorskaya

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Kong, L. et al. (2021). A USRP-Based Testbed of Multi-agent Reinforcement Learning for Dynamic Spectrum Anti-Jamming. In: Kountchev, R., Mahanti, A., Chong, S., Patnaik, S., Favorskaya, M. (eds) Advances in Wireless Communications and Applications. Smart Innovation, Systems and Technologies, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-15-5697-5_4

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  • DOI: https://doi.org/10.1007/978-981-15-5697-5_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5696-8

  • Online ISBN: 978-981-15-5697-5

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