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
    PublisherIEEE Institute of Electrical and Electronic Engineers
    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
    Country/TerritoryFrance
    CityParis
    Period1/04/124/04/12

    Fingerprint

    Dive into the research topics of 'Conjectural variations in multi-agent reinforcement learning for energy-efficient cognitive wireless mesh networks'. Together they form a unique fingerprint.

    Cite this