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A fully distributed learning algorithm for power allocation in heterogeneous networks

  • Hajar El HammoutiEmail author
  • Loubna Echabbi
  • Rachid El Azouzi
Article

Abstract

In this work, we present a fully distributed Learning algorithm for power allocation in HetNets, referred to as the FLAPH, that reaches the global optimum given by the total social welfare. Using a mix of macro and femto base stations, we discuss opportunities to maximize users global throughput. We prove the convergence of the algorithm and compare its performance with the well-established Gibbs and Max-logit algorithms which ensure convergence to the global optimum. Algorithms are compared in terms of computational complexity, memory space, and time convergence.

Keywords

Distributed algorithms HetNets Nash equilibrium Global optimum Gibbs-sampler Max-logit 

Mathematics Subject Classification

91Axx 

Notes

References

  1. 1.
    Ghosh A, Ratasuk R, Mondal B, Mangalvedhe N, Thomas T (2010) LTE-advanced: next-generation wireless broadband technology. IEEE Wirel Commun 17(3):10–22CrossRefGoogle Scholar
  2. 2.
    Advanced Wireless Technology Group (AWTG) (2013) Heterogeneous networks (HetNets) using small cells. White paperGoogle Scholar
  3. 3.
    Darmann A, Pferschy U, Schauer J (2010) Resource allocation with time intervals. Theor Comput Sci 411(49):4217–4234MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Adeane J, Rodrigues MRD, Wassell IJ (2005) Centralized and distributed power allocation algorithms in cooperative networks. In: IEEE 6th international workshop on signal processing advances in wireless communications (SPAWC), New York, pp 333–337Google Scholar
  5. 5.
    Li J, Chen X, Botella C, Svensson T, Eriksson T (2012) Resource allocation for OFDMA systems with multi-cell joint transmission. In: IEEE 13th international workshop on signal processing advances in wireless communications (SPAWC), Cesme, pp 179–183Google Scholar
  6. 6.
    Li J, Svensson T, Botella C, Eriksson T, Xu X, Chen X (2012) Joint scheduling and power control in coordinated multi-point clusters. In: IEEE vehicular technology conference (VTC Fall), Yokohama, pp 1–5Google Scholar
  7. 7.
    Kauffmann B, Baccelli F, Chaintreau A, Mhatre V, Papagiannaki K, Diot C (2007) Measurement-based self organization of interfering 802.11 wireless access networks. In: 26th IEEE international conference on computer communications (INFOCOM), Anchorage, pp 1451–1459Google Scholar
  8. 8.
    Chen C, Baccelli F (2010) Self-optimization in mobile cellular networks: power control and user association. In: IEEE international conference on communications (ICC), Cape Town, pp 1–6Google Scholar
  9. 9.
    Borst S, Markakis M, Saniee I (2011) Distributed power allocation and user assignment in OFDMA cellular networks. In: The annual conference on communication, control, and computing, Allerton Park, pp 46–64Google Scholar
  10. 10.
    Bertsimas D, Tsitsiklis J (1993) Simulated annealing. Stat Sci 8(1):10–15CrossRefzbMATHGoogle Scholar
  11. 11.
    Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13(2):311–329MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Song Y, Wong SHY, Lee K (2011) A Game theoretical approach to gateway selections in multi-domain wireless networks. In: Proceedings of the fifth annual conference of the international technology allianceGoogle Scholar
  13. 13.
    Xing Y, Maille P, Tuffin B, Chandramouli R (2009) User strategy learning when pricing a red buffer. Simul Model Pract Theor 17:548–557CrossRefGoogle Scholar
  14. 14.
    Raghunathan V, Kumar P (2004) On delay-adaptive routing in wireless networks. In: 44th IEEE conference on decision and control (CDC), Seville, pp 4661–4666Google Scholar
  15. 15.
    Tuffin B, Maille P (2006) How many parallel TCP sessions to open: a pricing perspective. In: ICQT 2006. LNCS, vol 4033. Springer, Heidelberg, pp 2–12Google Scholar
  16. 16.
    Barth D, Echabbi L, Hamlaoui C (2008) Optimal transit price negotiation: the distributed learning perspective. J Univ Comput Sci 14(5):745–765Google Scholar
  17. 17.
    Xing Y, Chandramouli R (2008) Stochastic learning solution for distributed discrete power control game in wireless data networks. IEEE ACM Trans Netw 16(4):932–944CrossRefGoogle Scholar
  18. 18.
    Shannon CE (1949) Communication in the presence of noise. In: Proceedings of the institute of radio engineers, pp 10–21Google Scholar
  19. 19.
    Borst S, Markakis M, Saniee I (2013) Nonconcave utility maximization in locally coupled systems, with applications to wireless and wireline networks. IEEE ACM Trans Netw 22(2):674–687CrossRefGoogle Scholar
  20. 20.
    Ahmed Khan M, Tembine H, Vasilakos AV (2012) Game dynamics and cost of learning in heterogeneous 4G networks. IEEE J Sel Areas Commun 30(1):198–213CrossRefGoogle Scholar
  21. 21.
    Hastings WK (1970) Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1):97–109MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Telecommunications Systems, Networks and Services, STRSNational Institute of Posts and TelecommunicationsRabatMorocco
  2. 2.Department and Laboratory of Informatique of Avignon, LIAUniversity of AvignonAvignonFrance

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