A Fully Distributed Learning Algorithm for Power Allocation in Heterogeneous Networks

  • Hajar ElhammoutiEmail author
  • Loubna Echabbi
  • Rachid Elazouzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9466)


In this work, we present a Fully distributed Learning Algorithm for Power allocation in HetNetS, referred to as FLAPH algorithm, that reaches to 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 our algorithm and compare its performances with the well-established Gibbs algorithm which ensures convergence to the global optimum.


Distributed algorithms HetNets Nash equilibrium Global optimum 


  1. 1.
    Damnjanovic, A., Montojo, J., Wei, Y., Ji, T., Luo, T., Vajapeyam, M., Yoo, T., Song, O., Malladi, D.: QualComm Inc.: A survey on 3GPP heterogeneous networks. Wirel. Commun. 1(3), 10–21 (2011)CrossRefGoogle Scholar
  2. 2.
    Darmann, A., Pferschy, U., Schauer, J.: Resource allocation with time intervals. Theor. Comput. Sci. 411(49), 4217–4234 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Ghosh, A., Ratasuk, R., Mondal, B., Mangalvedhe, N., Thomas, T.: TE-advanced: next-generation wireless broadband technology, LTE-advanced: next-generation wireless broadband technology. Wirel. Commun. 17(3), 10–22 (2010)CrossRefGoogle Scholar
  4. 4.
    Hajek, B.: Cooling schedules for optimal annealing. Math. Oper. Res. 13(2), 311–329 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Kauffmann, B., Baccelli, F., Chaintreau, A., Mhatre, V., Papagiannaki, K., Diot, C.: Measurement-based self organization of interfering 802.11 wireless access networks. In: 26th IEEE International Conference on Computer Communications INFOCOM 2007, pp 1451–1459, May 2007Google Scholar
  6. 6.
    Chen, C., Baccelli, F.: Self-optimization in mobile cellular networks: power control and user association. In: IEEE International Conference on Communications (ICC), pp 1–6, May 2010Google Scholar
  7. 7.
    Shannon, C.E.: Communication in the presence of noise. In: Proceedings of the Institute of Radio Engineers, pp. 10–21 (1949)Google Scholar
  8. 8.
    Barth, D., Echabbi, L., Hamlaoui, C.: Optimal transit price negotiation: the distributed learning perspective. J. Univers. Comput. Sci. 14(5), 745–765 (2008)Google Scholar
  9. 9.
    Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Stat. Sci. 8(1), 10–15 (1993)CrossRefzbMATHGoogle Scholar
  10. 10.
    Adeane, J., Rodrigues, M.R.D., Wassell, I.J.: Centralized and distributed power allocation algorithms in cooperative networks. In: 6th Workshop on Signal Processing Advances in Wireless Communications, pp. 333–337. IEEE (2005)Google Scholar
  11. 11.
    Li, J., Svensson, T., Botella, C., Eriksson, T., Xu, X., Chen, X.: Joint scheduling and power control in coordinated multi-point clusters. In: IEEE Vehicular Technology Conference (VTC Fall) 2012, pp. 1–5, November 2012Google Scholar
  12. 12.
    Li, J., Chen, X., Botella, C., Svensson, T., Eriksson, T.: Resource allocation for OFDMA systems with multi-cell joint transmission. In: IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2012, pp. 179–183, June 2012Google Scholar
  13. 13.
    Ahmed Khan, M., Tembine, H., Vasilakos, A.V.: Game dynamics and cost of learning in heterogeneous 4G networks. IEEE J. Sel. Areas Commun. 30(1), 198–213 (2012)CrossRefGoogle Scholar
  14. 14.
    Tuffin, B., Maillé, P.: How many parallel TCP sessions to open: a pricing perspective. In: Stiller, B., Reichl, P., Tuffin, B. (eds.) ICQT 2006. LNCS, vol. 4033, pp. 2–12. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  15. 15.
    Borst, S., Markakis, M., Saniee, I.: Distributed power allocation and user assignment in OFDMA cellular networks. In: The Annual Conference on Communication, Control, and Computing (Allerton), pp. 46–64, September 2011Google Scholar
  16. 16.
    Borst, S., Markakis, M., Saniee, I.: Nonconcave utility maximization in locally coupled systems, with applications to wireless and wireline networks. IEEE ACM Trans. Netw. 22(2), 674–687 (2013)CrossRefGoogle Scholar
  17. 17.
    Raghunathan, V., Kumar, P.: On delay-adaptive routing in wireless networks. In: Proceedings of CDC 2004 (2004)Google Scholar
  18. 18.
    Hastings, W.K.: Monte carlo sampling methods msing markov chains and their applications. Biometrika 57(1), 97–109 (1970)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Xing, Y., Maille, P., Tuffin, B., Chandramouli, R.: User strategy learning when pricing a red buffer. Simul. Model. Pract. Theor. 17, 548–557 (2009)CrossRefGoogle Scholar
  20. 20.
    Xing, Y., Chandramouli, R.: Stochastic learning solution for distributed discrete power control game in wireless data networks. IEEE ACM Trans. Netw. 16(4), 932–944 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hajar Elhammouti
    • 1
    Email author
  • Loubna Echabbi
    • 1
  • Rachid Elazouzi
    • 2
  1. 1.Department of Telecommunications Systems, Networks and Services, STRSNational Institute of Posts and TelecommunicationsRabatMorocco
  2. 2.Department Laboratory of Informatique of Avignon, LIAUniversity of AvignonAvignonFrance

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