Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks

X. Chen, Z. Zhao, H. Zhang, Tao Chen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

7 Citations (Scopus)

Abstract

As energy saving and environmental protection become an inevitable trend, researchers need to shift their focus to "green" oriented architecture design. Recent advances in the area of cognitive radio (CR) have significant potential towards "green" communications. One of the critical challenges for operating CRs in a wireless mesh network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of primary users. Due to the SUs' intelligent and selfish properties, this paper focuses on the non-cooperative spectrum sharing in cognitive wireless mesh networks formed by a number of clusters. In order to study the competition behaviors of SUs in a dynamic environment, the problem is modeled as a stochastic learning process. We first extend the single-agent reinforcement learning (RL) to a multi-user context, based on which a conjecture based multi-agent RL algorithm is proposed. A rational SU learns the optimal transmission strategy from the conjecture over the other SUs' responses
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIEEE Wireless Communications and Networking Conference, WCNC 2012
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages820-825
ISBN (Electronic)978-1-4673-0437-5
ISBN (Print)978-1-4673-0436-8
DOIs
Publication statusPublished - 2012
MoE publication typeA4 Article in a conference publication
EventIEEE Wireless Communications and Networking Conference, WCNC 2012 - Paris, France
Duration: 1 Apr 20124 Apr 2012

Conference

ConferenceIEEE Wireless Communications and Networking Conference, WCNC 2012
Abbreviated titleWCNC 2012
CountryFrance
CityParis
Period1/04/124/04/12

Fingerprint

Wireless mesh networks (WMN)
Reinforcement learning
Cognitive radio
Environmental protection
Power transmission
Learning algorithms
Energy conservation
Quality of service
Communication

Cite this

Chen, X., Zhao, Z., Zhang, H., & Chen, T. (2012). Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks. In Proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2012 (pp. 820-825). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/WCNC.2012.6214485
Chen, X. ; Zhao, Z. ; Zhang, H. ; Chen, Tao. / Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks. Proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2012. Institute of Electrical and Electronic Engineers IEEE, 2012. pp. 820-825
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Chen, X, Zhao, Z, Zhang, H & Chen, T 2012, Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks. in Proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2012. Institute of Electrical and Electronic Engineers IEEE, pp. 820-825, IEEE Wireless Communications and Networking Conference, WCNC 2012, Paris, France, 1/04/12. https://doi.org/10.1109/WCNC.2012.6214485

Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks. / Chen, X.; Zhao, Z.; Zhang, H.; Chen, Tao.

Proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2012. Institute of Electrical and Electronic Engineers IEEE, 2012. p. 820-825.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Chen X, Zhao Z, Zhang H, Chen T. Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks. In Proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2012. Institute of Electrical and Electronic Engineers IEEE. 2012. p. 820-825 https://doi.org/10.1109/WCNC.2012.6214485