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A Novel Low-Complexity Orthogonalization Method Read chapter Read first chapter

2021 | OriginalPaper | Chapter

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

Authors : Lijun Kong, Ximing Wang, Xufang Pei, Luliang Jia, Dianxiong Liu, Kailing Yao, Yuhua Xu

Published in: Advances in Wireless Communications and Applications

Publisher: Springer Singapore

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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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Metadata
Title
A USRP-Based Testbed of Multi-agent Reinforcement Learning for Dynamic Spectrum Anti-Jamming
Authors
Lijun Kong
Ximing Wang
Xufang Pei
Luliang Jia
Dianxiong Liu
Kailing Yao
Yuhua Xu
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Wireless Communications and Applications

Print ISBN: 978-981-15-5696-8

Electronic ISBN: 978-981-15-5697-5

Copyright Year: 2021

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