A fully distributed learning algorithm for power allocation in heterogeneous networks

Autor:	Hajar El Hammouti, Rachid El Azouzi, Loubna Echabbi
Rok vydání:	2019
Předmět:	Numerical Analysis Computational complexity theory Computer science 020206 networking & telecommunications 02 engineering and technology Computer Science Applications Theoretical Computer Science Power (physics) Computational Mathematics Global optimum Computational Theory and Mathematics Convergence (routing) Computer Science::Networking and Internet Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Distributed learning Macro Throughput (business) Algorithm Software Heterogeneous network
Zdroj:	Computing. 101:1287-1303
ISSN:	1436-5057 0010-485X
DOI:	10.1007/s00607-019-00700-z
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7e6b87ecb059b3ac36181c849297db70 https://doi.org/10.1007/s00607-019-00700-z Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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