Scheduling in Heterogeneous Networks Using Grammar-Based Genetic Programming

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

DOI: 10.1007/978-3-319-30668-1_6

Volume 9594 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Lynch D., Fenton M., Kucera S., Claussen H., O’Neill M. (2016) Scheduling in Heterogeneous Networks Using Grammar-Based Genetic Programming. In: Heywood M., McDermott J., Castelli M., Costa E., Sim K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science, vol 9594. Springer, Cham

Abstract

Effective scheduling in Heterogeneous Networks is key to realising the benefits from enhanced Inter-Cell Interference Coordination. In this paper we address the problem using Grammar-based Genetic Programming. Our solution executes on a millisecond timescale so it can track with changing network conditions. Furthermore, the system is trained using only those measurement statistics that are attainable in real networks. Finally, the solution generalises well with respect to dynamic traffic and variable cell placement. Superior results are achieved relative to a benchmark scheme from the literature, illustrating an opportunity for the further use of Genetic Programming in software-defined autonomic wireless communications networks.

Keywords

Scheduling Heterogeneous networks Grammar-based genetic programming 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • David Lynch
    • 1
  • Michael Fenton
    • 1
  • Stepan Kucera
    • 2
  • Holger Claussen
    • 2
  • Michael O’Neill
    • 1
  1. 1.Natural Computing Research and Applications GroupUCDDublinIreland
  2. 2.Bell LaboratoriesNOKIADublinIreland