Advertisement

Configuring Dynamic Heterogeneous Wireless Communications Networks Using a Customised Genetic Algorithm

  • David Lynch
  • Michael Fenton
  • Stepan Kucera
  • Holger Claussen
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Wireless traffic is surging due to the prevalence of smart devices, rising demand for multimedia content and the advent of the “Internet of Things”. Network operators are deploying Small Cells alongside existing Macro Cells in order to satisfy demand during this era of exponential growth. Such Heterogeneous Networks (HetNets) are highly spectrally efficient because both cell tiers transmit using the same scarce and expensive bandwidth. However, load balancing and cross-tier interference issues constrain cell-edge rates in co-channel operation. Capacity can be increased by intelligently configuring Small Cell powers and biases, and the muting cycles of Macro Cells. This paper presents a customised Genetic Algorithm (GA) for reconfiguring HetNets. The GA converges within minutes so tailored settings can be pushed to cells in real time. The proposed GA lifts cell-edge (2.5th percentile) rates by 32% over a non-adaptive baseline that is used in practice. HetNets are highly dynamic environments. However, customers tend to cluster in hotspots which arise at predictable locations over the course of a typical day. An explicit memory of previously evolved solutions is maintained and used to seed fresh runs. System level simulations show that the 2.5th percentile rates are boosted to 36% over baseline when prior knowledge is utilised.

Notes

Acknowledgements

This research is based upon works supported by the Science Foundation Ireland under grant 13/IA/1850. The authors are grateful to the reviewers and Dr. Miguel Nicolau for their helpful comments.

References

  1. 1.
    Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014–2019. Cisco, White Paper (online) (2015)Google Scholar
  2. 2.
    Aliu, O.G., Imran, A., Imran, M.A., Evans, B.: A survey of self organisation in future cellular networks. IEEE Commun. Surv. Tutorials 15(1), 336–361 (2013)CrossRefGoogle Scholar
  3. 3.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: In Congress on Evolutionary Computation CEC99. Citeseer (1999)Google Scholar
  4. 4.
    Branke, J.: Evolutionary Optimization in Dynamic Environments, vol. 3. Springer Science & Business Media, Berlin (2012)zbMATHGoogle Scholar
  5. 5.
    Claussen, H., Ho, L.: Multi-carrier cell structures with angular offset. In: 2012 IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pp. 1179–1184. IEEE (2012)Google Scholar
  6. 6.
    Deb, K., Myburgh, C.: Breaking the billion-variable barrier in real-world optimization using a customized evolutionary algorithm. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 653–660. ACM (2016)Google Scholar
  7. 7.
    Deb, S., Monogioudis, P., Miernik, J., Seymour, J.P.: Algorithms for enhanced Inter-cell Interference Coordination (eICIC) in LTE HetNets. IEEE/ACM Trans. Netw. (TON) 22(1), 137–150 (2014)CrossRefGoogle Scholar
  8. 8.
    Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments, vol. 194. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Fenton, M., Lynch, D., Kucera, S., Claussen, H., O’Neill, M.: Evolving coverage optimisation functions for heterogeneous networks using grammatical genetic programming. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 219–234. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-31204-0_15CrossRefGoogle Scholar
  10. 10.
    Fenton, M., Lynch, D., Kucera, S., Claussen, H., O’Neill, M.: Load balancing in heterogeneous networks using an evolutionary algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 70–76. IEEE (2015)Google Scholar
  11. 11.
    Hämäläinen, S., Sanneck, H., Sartori, C.: LTE Self-organising Networks (SON): Network Management Automation for Operational Efficiency. Wiley, Hoboken (2012)Google Scholar
  12. 12.
    Karaman, A., Uyar, Ş., Eryiğit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 563–573. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-32003-6_59CrossRefGoogle Scholar
  13. 13.
    Liang, Y.: Real-time VBR video traffic prediction for dynamic bandwidth allocation. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 34(1), 32–47 (2004)CrossRefGoogle Scholar
  14. 14.
    López-Pérez, D., Claussen, H.: Duty cycles and load balancing in HetNets with eICIC almost blank Subframes. In: 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), pp. 173–178. IEEE (2013)Google Scholar
  15. 15.
    Lopez-Perez, D., Guvenc, I., De la Roche, G., Kountouris, M., Quek, T.Q., Zhang, J.: Enhanced intercell interference coordination challenges in heterogeneous networks. IEEE Wirel. Commun. 18(3), 22–30 (2011)CrossRefGoogle Scholar
  16. 16.
    Madan, R., Borran, J., Sampath, A., Bhushan, N., Khandekar, A., Ji, T.: Cell association and interference coordination in heterogeneous LTE-a cellular networks. IEEE J. Sel. Areas Commun. 28(9), 1479–1489 (2010)CrossRefGoogle Scholar
  17. 17.
    Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer Science & Business Media, Berlin (2013)zbMATHGoogle Scholar
  18. 18.
    Peng, M., Liang, D., Wei, Y., Li, J., Chen, H.H.: Self-configuration and self-optimization in LTE-advanced heterogeneous networks. IEEE Commun. Mag. 51(5), 36–45 (2013)CrossRefGoogle Scholar
  19. 19.
    Ramsey, C.L., Grefenstette, J.J.: Case-based initialization of genetic algorithms. In: ICGA, pp. 84–91. Citeseer (1993)Google Scholar
  20. 20.
    Shannon, C.E.: Communication in the presence of noise. Proc. IRE 37(1), 10–21 (1949)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Tall, A., Altman, Z., Altman, E.: Self organizing strategies for enhanced ICIC (eICIC). In: 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), pp. 318–325. IEEE (2014)Google Scholar
  22. 22.
    Tang, J., So, D.K., Alsusa, E., Hamdi, K.A., Shojaeifard, A.: Resource allocation for energy efficiency optimization in heterogeneous networks. IEEE J. Sel. Areas Commun. 33(10), 2104–2117 (2015)CrossRefGoogle Scholar
  23. 23.
    Winstein, K., Sivaraman, A., Balakrishnan, H.: Stochastic forecasts achieve high throughput and low delay over cellular networks. In: Presented as part of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2013), pp. 459–471 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

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 Group, UCDDublinIreland
  2. 2.Bell Laboratories Nokia-DublinDublinIreland

Personalised recommendations