Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming

  • Michael Fenton
  • David Lynch
  • Stepan Kucera
  • Holger Claussen
  • Michael O’Neill
Conference paper

DOI: 10.1007/978-3-319-31204-0_15

Volume 9597 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Fenton M., Lynch D., Kucera S., Claussen H., O’Neill M. (2016) Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming. In: Squillero G., Burelli P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597. Springer, Cham

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.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michael Fenton
    • 1
  • David Lynch
    • 1
  • Stepan Kucera
    • 2
  • Holger Claussen
    • 2
  • Michael O’Neill
    • 1
  1. 1.Natural Computing Research and Applications Group, UCDDublinIreland
  2. 2.Bell Laboratories, Alcatel-LucentDublinIreland