A learning approach for traffic offloading in stochastic heterogeneous cellular networks

Xianfu Chen, Celimuge Wu, Yifan Zhou, Honggang Zhang

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

6 Citations (Scopus)

Abstract

This paper addresses energy-aware traffic offloading in stochastic heterogeneous cellular networks (HCNs). The objective is to minimize energy consumption of the HCN while maintaining Quality-of-Service experienced by the mobile users. For each cell, the energy consumption depends on its associated system load, which is coupled with system loads in other cells due to the sharing over a common spectrum band. Such a traffic offloading problem is modeled by a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller learns to solve the optimal traffic offloading strategy with no prior knowledge of the DTMDP statistics. To deal with the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized QC-learning algorithm is developed such that the macro-cell base stations (BSs) can independently manage the operations of small-cell BSs by making use of the network information obtained from the network controller. Simulations validate the proposed studies.
Original languageEnglish
Title of host publicationCommunications (ICC), 2015 IEEE International Conference on
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages3347-3351
ISBN (Electronic)978-1-4673-6432-4, 978-1-4673-6431-7
DOIs
Publication statusPublished - 10 Sep 2015
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Communications: Smart City & Smart World - London, United Kingdom
Duration: 8 Jun 201512 Jun 2015

Publication series

Name
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883

Conference

ConferenceIEEE International Conference on Communications
Abbreviated titleICC
CountryUnited Kingdom
CityLondon
Period8/06/1512/06/15

Fingerprint

Base stations
Energy utilization
Controllers
Learning algorithms
Macros
Quality of service
Statistics

Keywords

  • heterogeneous cellular networks
  • HCN

Cite this

Chen, X., Wu, C., Zhou, Y., & Zhang, H. (2015). A learning approach for traffic offloading in stochastic heterogeneous cellular networks. In Communications (ICC), 2015 IEEE International Conference on (pp. 3347-3351). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ICC.2015.7248841
Chen, Xianfu ; Wu, Celimuge ; Zhou, Yifan ; Zhang, Honggang. / A learning approach for traffic offloading in stochastic heterogeneous cellular networks. Communications (ICC), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. pp. 3347-3351
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Chen, X, Wu, C, Zhou, Y & Zhang, H 2015, A learning approach for traffic offloading in stochastic heterogeneous cellular networks. in Communications (ICC), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, pp. 3347-3351, IEEE International Conference on Communications, London, United Kingdom, 8/06/15. https://doi.org/10.1109/ICC.2015.7248841

A learning approach for traffic offloading in stochastic heterogeneous cellular networks. / Chen, Xianfu; Wu, Celimuge; Zhou, Yifan; Zhang, Honggang.

Communications (ICC), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. p. 3347-3351.

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

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N2 - This paper addresses energy-aware traffic offloading in stochastic heterogeneous cellular networks (HCNs). The objective is to minimize energy consumption of the HCN while maintaining Quality-of-Service experienced by the mobile users. For each cell, the energy consumption depends on its associated system load, which is coupled with system loads in other cells due to the sharing over a common spectrum band. Such a traffic offloading problem is modeled by a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller learns to solve the optimal traffic offloading strategy with no prior knowledge of the DTMDP statistics. To deal with the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized QC-learning algorithm is developed such that the macro-cell base stations (BSs) can independently manage the operations of small-cell BSs by making use of the network information obtained from the network controller. Simulations validate the proposed studies.

AB - This paper addresses energy-aware traffic offloading in stochastic heterogeneous cellular networks (HCNs). The objective is to minimize energy consumption of the HCN while maintaining Quality-of-Service experienced by the mobile users. For each cell, the energy consumption depends on its associated system load, which is coupled with system loads in other cells due to the sharing over a common spectrum band. Such a traffic offloading problem is modeled by a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller learns to solve the optimal traffic offloading strategy with no prior knowledge of the DTMDP statistics. To deal with the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized QC-learning algorithm is developed such that the macro-cell base stations (BSs) can independently manage the operations of small-cell BSs by making use of the network information obtained from the network controller. Simulations validate the proposed studies.

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Chen X, Wu C, Zhou Y, Zhang H. A learning approach for traffic offloading in stochastic heterogeneous cellular networks. In Communications (ICC), 2015 IEEE International Conference on. Institute of Electrical and Electronic Engineers IEEE. 2015. p. 3347-3351 https://doi.org/10.1109/ICC.2015.7248841