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Mean-Field Power Allocation for UDN

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)

Abstract

Ultra Dense Network (UDN) is an effective solution to the explosive growth of traffic in the future 5G networks. In this paper, a mean-field power allocation algorithm is proposed for UDN. It imbeds the power allocation decision problem into a Dynamic Stochastic Game (DSG) model. And then it finds the optimal decision by deriving the model into a mean-field game model. The simulation results show that compared with the other methods, the proposed method can achieve better performance in terms of the CDF and the Utility EE, and can also guarantee the Quality of Service (QoS).

Keywords

Mean-field theory Ultra dense network Power control Dynamic stochastic game 

Notes

Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No. 61501306), Doctoral Scientific Research Foundation of Liaoning Province (Grant No. 20170520228), College Students’ innovation and entrepreneurship training program(Grant No. 110418092).

References

  1. 1.
    de Mari M, Calvanese Strinati E, Debbah M, Quek TQS (2017) Joint stochastic geometry and mean field game optimization for energy-efficient proactive scheduling in ultra dense networks. IEEE Trans Commun Netw 3(4):766–781; Voronkov A (2004) EasyChair conference system. Retrieved from easychair.orgGoogle Scholar
  2. 2.
    Aziz M, Caines PE (2017) A mean field game computational methodology for decentralized cellular network optimization. IEEE Trans Control Syst Technol 25(2):563–576CrossRefGoogle Scholar
  3. 3.
    Samarakoon S, Bennis M, Saad W, Debbah M, Latva-aho M (2015) Energy-efficient resource management in ultra dense small cell networks: a mean-field approach. In: 2015 IEEE global communications conference (GLOBECOM), San Diego, CA, pp 1–6Google Scholar
  4. 4.
    Yang C, Li J, Sheng M, Anpalagan A, Xiao J (2018) Mean field game-theoretic framework for interference and energy-aware control in 5G ultra-dense networks. IEEE Wirel Commun 25(1):114–121CrossRefGoogle Scholar
  5. 5.
    Yang C, Li J, Guizani M (2016) Cooperation for spectral and energy efficiency in ultra-dense small cell networks. IEEE Wireless Commun. 23:64–71CrossRefGoogle Scholar
  6. 6.
    Al-Zahrani AY, Yu FR, Huang M (2016) A joint cross-layer and colayer interference management scheme in hyperdense heterogeneous networks using mean-field game theory. IEEE Trans Veh Technol 65(3):1522–1535CrossRefGoogle Scholar
  7. 7.
    Park J, Jung SY, Kim SL, Bennis M, Debbah M (2016) User-centric mobility management in ultra-dense cellular networks under spatio-temporal dynamics. In: Proceedings of IEEE global communication conference (GLOBECOM), pp 1–6Google Scholar
  8. 8.
    Xiao Y, Niyato D, Han Z, DaSilva LA (2015) Dynamic energy trading for energy harvesting communication networks: a stochastic energy trading game. IEEE J Sel Areas Commun 33(12):2718–2734CrossRefGoogle Scholar
  9. 9.
    de Mari M, Calvanese Strinati E, Debbah M, Quek TQS (2017) Joint stochastic geometry and mean field game optimization for energy-efficient proactive scheduling in ultra dense networks. IEEE Trans Cognitive Commun Netw 3(4):766–781CrossRefGoogle Scholar
  10. 10.
    Shafigh AS, Mertikopoulos P, Glisic S (2016) A novel dynamic network architecture model based on stochastic geometry and game theory. In: 2016 IEEE international conference on communications (ICC), Kuala Lumpur, pp 1–7Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringShenyang Aerospace UniversityShenyangChina
  2. 2.Department of Air Defense ForcesNoncommissioned Officer Academy, Institute of Army Artillery and Air Defense ForcesShenyangChina

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