Abstract
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.
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Elhammouti, H., Echabbi, L., Elazouzi, R. (2015). A Fully Distributed Learning Algorithm for Power Allocation in Heterogeneous Networks. In: Bouajjani, A., Fauconnier, H. (eds) Networked Systems . NETYS 2015. Lecture Notes in Computer Science(), vol 9466. Springer, Cham. https://doi.org/10.1007/978-3-319-26850-7_15
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DOI: https://doi.org/10.1007/978-3-319-26850-7_15
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