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
PublisherInstitute of Electrical and Electronic Engineers IEEE
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

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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 Institute of Electrical and Electronic Engineers IEEE. 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. Institute of Electrical and Electronic Engineers IEEE, 2013.
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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.",
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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. Institute of Electrical and Electronic Engineers IEEE, 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. Institute of Electrical and Electronic Engineers IEEE, 2013.

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

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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.

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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. Institute of Electrical and Electronic Engineers IEEE. 2013 https://doi.org/10.1109/PIMRC.2013.6666295