Advertisement

An effective hyper-dense deployment algorithm via search economics

  • Chun-Wei TsaiEmail author
  • Shin-Jui Liu
Original Research
  • 8 Downloads

Abstract

Developing an effective strategy for deploying base stations of a mobile communication environment has been a critical issue for years because it typically needs to take into account several conflict factors, such as coverage ratio and interference. Since 5G cellular services are expected to be commercially available in 2020, a “good deployment strategy” for the hyper-dense deployment problem (HDDP) has attracted the attention of researchers from different disciplines in recent years. To enhance the performance of a 5G mobile communication environment, an effective search algorithm for solving the HDDP, called search economics for hyper-dense deployment problem (SE-HDDP), is presented in this paper. A distinctive feature of the proposed algorithm is that it divides the search space into a set of subspaces and dynamically allocates the computing resources to these subspaces based on their potentials during the convergence process. The simulation results show that the proposed algorithm is able to find a better result of HDDP for a 5G mobile communication environment than all the other metaheuristic and rule-based algorithms compared in this paper.

Keywords

Mobile communication Deployment problem Metaheuristic algorithm Search economics 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST106-2221-E-005-094 and MOST107-2221-E-110-078.

References

  1. Agiwal M, Roy A, Saxena N (2016) Next generation 5G wireless networks: a comprehensive survey. IEEE Commun Surv Tutor 18(3):1617–1655CrossRefGoogle Scholar
  2. Amzallag D, Livschitz M, Naor J, Raz D (2005) Cell planning of 4G cellular networks: algorithmic techniques and results. In: Proceedings of the IEE international conference on 3G and beyond, pp 1–5Google Scholar
  3. Andreev S, Galinina O, Pyattaev A, Gerasimenko M, Tirronen T, Torsner J, Sachs J, Dohler M, Koucheryavy Y (2015) Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap. IEEE Commun Mag 53(9):32–40CrossRefGoogle Scholar
  4. Barona López LI, Maestre Vidal J, García Villalba LJ (2018) Orchestration of use-case driven analytics in 5G scenarios. J Ambient Intell Humaniz Comput 9(4):1097–1117CrossRefGoogle Scholar
  5. Blum C, Puchinger J, Raidl GR, Roli A (2010) A brief survey on hybrid metaheuristics. In: Proceedings of the international conference on bioinspired optimization methods and their applications, pp 3–18Google Scholar
  6. Bouras C, Diles G, Kokkinos V, Papazois A (2015) Optimizing hybrid access femtocell clusters in 5G networks. In: Proceedings of international conference on broadband and wireless computing, communication and applications, pp 220–226Google Scholar
  7. Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic Publishers, Boston, MA, pp 457–474CrossRefGoogle Scholar
  8. Chen Y, Duan L, Zhang Q (2015) Financial analysis of 4G network deployment. In: Proceedings of the IEEE international conference on computer communications, pp 1607–1615Google Scholar
  9. Cisco (2017) Cisco visual networking index: forecast and methodology, 2016–2021. Tech. rep., White Papers, https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
  10. Hong S, Brand J, Choi JI, Jain M, Mehlman J, Katti S, Levis P (2014) Applications of self-interference cancellation in 5G and beyond. IEEE Commun Mag 52(2):114–121CrossRefGoogle Scholar
  11. Lee CY, Kang HG (2000) Cell planning with capacity expansion in mobile communications: a tabu search approach. IEEE Trans Veh Technol 49(5):1678–1691CrossRefGoogle Scholar
  12. Li S, Xu LD, Zhao S (2018) 5G internet of things: a survey. J Ind Inform Integr 10:1–9Google Scholar
  13. Li X, Tang X, Wang C, Lin X (2013) Gibbs-sampling-based optimization for the deployment of small cells in 3G heterogeneous networks. In: Proceedings of the international symposium and workshops on modeling and optimization in mobile, ad hoc and wireless networks, pp 444–451Google Scholar
  14. Liu SJ, Tsai CW (2018) An effective search algorithm for hyper-dense deployment problem of 5g. In: The 9th international conference on emerging ubiquitous systems and pervasive networks, vol 141, pp 151 – 158Google Scholar
  15. Lozano M, García-Martínez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37(3):481–497MathSciNetCrossRefzbMATHGoogle Scholar
  16. Palattella MR, Dohler M, Grieco A, Rizzo G, Torsner J, Engel T, Ladid L (2016) Internet of things in the 5G era: enablers, architecture, and business models. IEEE J Sel Areas Commun 34(3):510–527CrossRefGoogle Scholar
  17. Qiu T, Wang X, Chen C, Atiquzzaman M, Liu L (2018) TMED: a spider-web-like transmission mechanism for emergency data in vehicular ad hoc networks. IEEE Trans Veh Technol 67(9):8682–8694CrossRefGoogle Scholar
  18. Ratasuk R, Prasad A, Li Z, Ghosh A, Uusitalo MA (2015) Recent advancements in M2M communications in 4G networks and evolution towards 5G. In: Proceedings of the international conference on intelligence in next generation networks, pp 52–57Google Scholar
  19. Sangaiah AK, Suraki MY, Sadeghilalimi M, Bozorgi SM, Hosseinabadi AAR, Wang J (2019) A new meta-heuristic algorithm for solving the flexible dynamic job-shop problem with parallel machines. Symmetry 11(2):1–17CrossRefGoogle Scholar
  20. Shafi M, Molisch AF, Smith PJ, Haustein T, Zhu P, Silva PD, Tufvesson F, Benjebbour A, Wunder G (2017) 5G: a tutorial overview of standards, trials, challenges, deployment, and practice. IEEE J Sel Areas Commun 35(6):1201–1221CrossRefGoogle Scholar
  21. Shiu LC, Lee CY, Yang CS (2011) The divide-and-conquer deployment algorithm based on triangles for wireless sensor networks. IEEE Sens J 11(3):781–790CrossRefGoogle Scholar
  22. Tsai CW (2015) Search economics: A solution space and computing resource aware search method. In: Proceedings of IEEE international conference on systems, man, and cybernetics, pp 2555–2560Google Scholar
  23. Tsai CW (2016) An effective WSN deployment algorithm via search economics. Comput Netw 101:178–191CrossRefGoogle Scholar
  24. Tsai CW, Cho HH, Shih TK, Pan JS, Rodrigues JJPC (2015) Metaheuristics for the deployment of 5G. IEEE Wirel Commun 22(6):40–46CrossRefGoogle Scholar
  25. Tutschku K (1998) Demand-based radio network planning of cellular mobile communication systems. Proc Conf Comput Commun 3:1054–1061Google Scholar
  26. Venticinque S, Amato A (2019) A methodology for deployment of iot application in fog. Journal of Ambient Intelligence and Humanized Computing 10(5):1955–1976CrossRefGoogle Scholar
  27. Wang CX, Haider F, Gao X, You XH, Yang Y, Yuan D, Aggoune HM, Haas H, Fletcher S, Hepsaydir E (2014) Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun Mag 52(2):122–130CrossRefGoogle Scholar
  28. Wu J, Rangan S, Zhang H (2016) Green communications: theoretical fundamentals, algorithms, and applications. CRC Press Inc, Boca RatonCrossRefGoogle Scholar
  29. Xu J, Wang J, Zhu Y, Yang Y, Zheng X, Wang S, Liu L, Horneman K, Teng Y (2014) Cooperative distributed optimization for the hyper-dense small cell deployment. IEEE Commun Mag 52(5):61–67CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan
  2. 2.Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichungTaiwan

Personalised recommendations