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Multi-user dynamic spectrum access based on reinforcement learning
Author(s): Jinming Xu; Zheng Dou; Lin Qi
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Paper Abstract

The quantity of radio communication service is growing up with the development of wireless communication technology. While limited spectrum allocation, inefficient utilization of frequency band and plenty of unused spectrum have led to spectrum scarcity. To solve this issue, we study dynamic spectrum access technique to maximize the utilization of multi-channel wireless network. we assume that N users sharing K channels, and the users can choose any channel to transmit. each user selects a channel and transmits a packet with a certain attempt probability. After each time slot, every user receives a binary observation which indicates the result of their transmission. We aim to find an optimal strategy for spectrum access, which maximizes channel utilization in a partial observation scheme without message exchange or online coordination between users. Because of the larger scale of state space and incomplete partial observation, acquiring the optimal solution is computationally expensive in general. To solve this issue, we develop a partial observation dynamic spectrum access algorithm based on deep reinforcement leaning. We take experiments to show the strong performance of the algorithm which can increase the channel utilization to 90% in partial observation scheme.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113732Y (3 January 2020); doi: 10.1117/12.2557620
Show Author Affiliations
Jinming Xu, Harbin Engineering Univ. (China)
Zheng Dou, Harbin Engineering Univ. (China)
Lin Qi, Harbin Engineering Univ. (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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