Online evolution of femtocell coverage algorithms using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Lester Ho and Holger Claussen and Davide Cherubini",
  title =        "Online evolution of femtocell coverage algorithms
                 using genetic programming",
  booktitle =    "24th IEEE International Symposium on Personal Indoor
                 and Mobile Radio Communications (PIMRC 2013)",
  year =         "2013",
  month =        sep,
  pages =        "3033--3038",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/PIMRC.2013.6666667",
  ISSN =         "2166-9570",
  abstract =     "The wide adoption of smart-phones has resulted in an
                 exponential increase in the demand for wireless data.
                 To address this problem, operators have started
                 deploying large numbers of small cells. In order to
                 operate such small cell network cost-effectively they
                 need to be able to intelligently optimise their
                 configuration, which can be achieved by applying
                 machine learning techniques such as genetic
                 programming. The use of genetic programming has
                 previously been used to derive joint coverage
                 algorithms for a group of enterprise femtocells.
                 However, the evolution of the algorithms was performed
                 in an offline manner, on a pre-defined simulation model
                 of the deployment scenario. In this paper, an approach
                 to perform the evolution in an on-line manner using an
                 automated model building process is presented. The
                 model building process uses network traces as inputs to
                 create a hierarchical Markov model that is shown to be
                 able to capture the behaviour of the femtocell network
                 well. It is shown that the resulting environment model
                 can effectively drive the on-line evolution of coverage
                 optimisation algorithms.",
  notes =        "Also known as \cite{6666667}",

Genetic Programming entries for Lester T W Ho Holger Claussen Davide Cherubini