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Evolving Femtocell Algorithms with Dynamic and Stationary Training Scenarios

  • Erik Hemberg
  • Lester Ho
  • Michael O’Neill
  • Holger Claussen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)

Abstract

We analyse the impact of dynamic training scenarios when evolving algorithms for femtocells, which are low power, low-cost, user-deployed cellular base stations. Performance is benchmarked against an alternative stationary training strategy where all scenarios are presented to each individual in the evolving population during each fitness evaluation. In the dynamic setup, different training scenarios are gradually exposed to the population over successive generations. The results show that the solutions evolved using the stationary training scenarios have the best out-of-sample performance. Moreover, the use of a grammar which produces discrete changes to the pilot power generate better solutions on the training and out-of-sample scenarios.

Keywords

Genetic Programming Tabu List Grammatical Evolution Training Scenario Macrocell User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Erik Hemberg
    • 1
  • Lester Ho
    • 2
  • Michael O’Neill
    • 1
  • Holger Claussen
    • 2
  1. 1.Natural Computing Research & Applications Group, Complex & Adaptive Systems Laboratory, School of Computer Science & InformaticsUniversity College DublinIreland
  2. 2.Bell LaboratoriesAlcatel-LucentDublinIreland

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