Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  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 =          "",
  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
  notes =        "PonyGE2 Python",

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