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A fully distributed learning algorithm for power allocation in heterogeneous networks

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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.

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Authors and Affiliations

  1. Department of Telecommunications Systems, Networks and Services, STRS, National Institute of Posts and Telecommunications, Rabat, Morocco

    Hajar El Hammouti & Loubna Echabbi

  2. Department and Laboratory of Informatique of Avignon, LIA, University of Avignon, Avignon, France

    Rachid El Azouzi

Authors
  1. Hajar El Hammouti
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  2. Loubna Echabbi
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  3. Rachid El Azouzi
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Correspondence to Hajar El Hammouti.

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El Hammouti, H., Echabbi, L. & El Azouzi, R. A fully distributed learning algorithm for power allocation in heterogeneous networks. Computing 101, 1287–1303 (2019). https://doi.org/10.1007/s00607-019-00700-z

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  • DOI: https://doi.org/10.1007/s00607-019-00700-z

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