Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Abstract In cognitive radio (CR) ad-hoc network, the characteristics of the frequency resources that vary with the time and geographical location need to be considered in order to efficiently use them. Environmental statistics, such as an available transmission opportunity and data rate for each cha...

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Detalhes bibliográficos
Principais autores: Sung-Jeen Jang, Chul-Hee Han, Kwang-Eog Lee, Sang-Jo Yoo
Formato: Artigo
Idioma:English
Publicado em: SpringerOpen 2019-05-01
coleção:EURASIP Journal on Wireless Communications and Networking
Assuntos:
Reinforcement learning
Cognitive radio
Ad-hoc network
Q-learning
Fairness
Acesso em linha:http://link.springer.com/article/10.1186/s13638-019-1433-1

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