Combined learning for resource allocation in autonomous heterogeneous cellular networks

Xianfu Chen, H. Zhang, Tao Chen, J. Palicot

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

    2 Citations (Scopus)

    Abstract

    The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationIEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4673-6235-1
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    Event24th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC'13 - London, United Kingdom
    Duration: 8 Nov 201311 Nov 2013
    Conference number: 24

    Conference

    Conference24th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC'13
    Abbreviated titlePIMRC'13
    CountryUnited Kingdom
    CityLondon
    Period8/11/1311/11/13

    Fingerprint

    Resource allocation
    Energy efficiency
    Costs

    Cite this

    Chen, X., Zhang, H., Chen, T., & Palicot, J. (2013). Combined learning for resource allocation in autonomous heterogeneous cellular networks. In Proceedings: IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013 IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/PIMRC.2013.6666295
    Chen, Xianfu ; Zhang, H. ; Chen, Tao ; Palicot, J. / Combined learning for resource allocation in autonomous heterogeneous cellular networks. Proceedings: IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013. IEEE Institute of Electrical and Electronic Engineers , 2013.
    @inproceedings{4191f9bc5c7549fbb1e419f19d34cd7c,
    title = "Combined learning for resource allocation in autonomous heterogeneous cellular networks",
    abstract = "The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.",
    author = "Xianfu Chen and H. Zhang and Tao Chen and J. Palicot",
    note = "Project code: 82164",
    year = "2013",
    doi = "10.1109/PIMRC.2013.6666295",
    language = "English",
    booktitle = "Proceedings",
    publisher = "IEEE Institute of Electrical and Electronic Engineers",
    address = "United States",

    }

    Chen, X, Zhang, H, Chen, T & Palicot, J 2013, Combined learning for resource allocation in autonomous heterogeneous cellular networks. in Proceedings: IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013. IEEE Institute of Electrical and Electronic Engineers , 24th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC'13, London, United Kingdom, 8/11/13. https://doi.org/10.1109/PIMRC.2013.6666295

    Combined learning for resource allocation in autonomous heterogeneous cellular networks. / Chen, Xianfu; Zhang, H.; Chen, Tao; Palicot, J.

    Proceedings: IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013. IEEE Institute of Electrical and Electronic Engineers , 2013.

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

    TY - GEN

    T1 - Combined learning for resource allocation in autonomous heterogeneous cellular networks

    AU - Chen, Xianfu

    AU - Zhang, H.

    AU - Chen, Tao

    AU - Palicot, J.

    N1 - Project code: 82164

    PY - 2013

    Y1 - 2013

    N2 - The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.

    AB - The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.

    U2 - 10.1109/PIMRC.2013.6666295

    DO - 10.1109/PIMRC.2013.6666295

    M3 - Conference article in proceedings

    BT - Proceedings

    PB - IEEE Institute of Electrical and Electronic Engineers

    ER -

    Chen X, Zhang H, Chen T, Palicot J. Combined learning for resource allocation in autonomous heterogeneous cellular networks. In Proceedings: IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013. IEEE Institute of Electrical and Electronic Engineers . 2013 https://doi.org/10.1109/PIMRC.2013.6666295