Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming

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@Article{Fenton:ieeeTCyB,

• author = "Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill",
• title = "Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming",
• journal = "IEEE Transactions on Cybernetics",
• year = "2017",
• volume = "47",
• number = "9",
• pages = "2938--2950",
• month = sep,
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Evolutionary computation, wireless communications networks",
• ISSN = "2168-2267",
• URL = "http://ieeexplore.ieee.org/abstract/document/7893786/",
• DOI = "doi:10.1109/TCYB.2017.2688280",
• size = "13 pages",
• abstract = "Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.",
• notes = "PonyGE2 Python",

}

Genetic Programming entries for Michael Fenton David Lynch Stepan Kucera Holger Claussen Michael O'Neill

Citations