2019
DOI: 10.1007/s00607-019-00700-z
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A fully distributed learning algorithm for power allocation in heterogeneous networks

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 comput… Show more

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