, Volume 101, Issue 9, pp 1287–1303 | Cite as

A fully distributed learning algorithm for power allocation in heterogeneous networks

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


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.


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

Mathematics Subject Classification




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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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