Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/evoW/FentonLKCO16,
  author =       "Michael Fenton and David Lynch and Stepan Kucera and 
                 Holger Claussen and Michael O'Neill",
  title =        "Evolving Coverage Optimisation Functions for
                 Heterogeneous Networks Using Grammatical Genetic
                 Programming",
  booktitle =    "19th European Conference on Applications of
                 Evolutionary Computation, EvoApplications 2016",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9597",
  series =       "Lecture Notes in Computer Science",
  pages =        "219--234",
  address =      "Porto, Portugal",
  month =        mar # " 30 -- " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2016-03-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#FentonLKCO16",
  isbn13 =       "978-3-319-31204-0",
  DOI =          "doi:10.1007/978-3-319-31204-0_15",
  abstract =     "Heterogeneous Cellular Networks are multi-tiered
                 cellular networks comprised of Macro Cells and Small
                 Cells in which all cells occupy the same bandwidth.
                 User Equipments greedily attach to whichever cell
                 provides the best signal strength. While Macro Cells
                 are invariant, the power and selection bias for each
                 Small Cell can be increased or decreased (subject to
                 pre-defined limits) such that more or fewer UEs attach
                 to that cell. Setting optimal power and selection bias
                 levels for Small Cells is key for good network
                 performance. The application of Genetic Programming
                 techniques has been proven to produce good results in
                 the control of Heterogenous Networks. Expanding on
                 previous works, this paper uses grammatical GP to
                 evolve distributed control functions for Small Cells in
                 order to vary their power and bias settings. The
                 objective of these control functions is to evolve
                 control functions that maximise a proportional fair
                 utility of UE throughputs.",
  notes =        "EvoApplications2016 held inconjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
}

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

Citations