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Published in: Computing 9/2019

11-02-2019

A fully distributed learning algorithm for power allocation in heterogeneous networks

Authors: Hajar El Hammouti, Loubna Echabbi, Rachid El Azouzi

Published in: Computing | Issue 9/2019

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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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Metadata
Title
A fully distributed learning algorithm for power allocation in heterogeneous networks
Authors
Hajar El Hammouti
Loubna Echabbi
Rachid El Azouzi
Publication date
11-02-2019
Publisher
Springer Vienna
Published in
Computing / Issue 9/2019
Print ISSN: 0010-485X
Electronic ISSN: 1436-5057
DOI
https://doi.org/10.1007/s00607-019-00700-z

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