Skip to main content
Log in

An effective hyper-dense deployment algorithm via search economics

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Agiwal M, Roy A, Saxena N (2016) Next generation 5G wireless networks: a comprehensive survey. IEEE Commun Surv Tutor 18(3):1617–1655

    Article  Google Scholar 

  • 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 1–5

  • 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):32–40

    Article  Google Scholar 

  • 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):1097–1117

    Article  Google Scholar 

  • 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 3–18

  • 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 220–226

  • 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 457–474

    Chapter  Google Scholar 

  • Chen Y, Duan L, Zhang Q (2015) Financial analysis of 4G network deployment. In: Proceedings of the IEEE international conference on computer communications, pp 1607–1615

  • 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

  • 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):114–121

    Article  Google Scholar 

  • Lee CY, Kang HG (2000) Cell planning with capacity expansion in mobile communications: a tabu search approach. IEEE Trans Veh Technol 49(5):1678–1691

    Article  Google Scholar 

  • Li S, Xu LD, Zhao S (2018) 5G internet of things: a survey. J Ind Inform Integr 10:1–9

    Google Scholar 

  • 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 444–451

  • 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 – 158

    Article  Google Scholar 

  • 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):481–497

    Article  MathSciNet  Google Scholar 

  • 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):510–527

    Article  Google Scholar 

  • 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):8682–8694

    Article  Google Scholar 

  • 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 52–57

  • 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):1–17

    Article  Google Scholar 

  • 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):1201–1221

    Article  Google Scholar 

  • 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):781–790

    Article  Google Scholar 

  • 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 2555–2560

  • Tsai CW (2016) An effective WSN deployment algorithm via search economics. Comput Netw 101:178–191

    Article  Google Scholar 

  • Tsai CW, Cho HH, Shih TK, Pan JS, Rodrigues JJPC (2015) Metaheuristics for the deployment of 5G. IEEE Wirel Commun 22(6):40–46

    Article  Google Scholar 

  • Tutschku K (1998) Demand-based radio network planning of cellular mobile communication systems. Proc Conf Comput Commun 3:1054–1061

    Google Scholar 

  • Venticinque S, Amato A (2019) A methodology for deployment of iot application in fog. Journal of Ambient Intelligence and Humanized Computing 10(5):1955–1976

    Article  Google Scholar 

  • 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):122–130

    Article  Google Scholar 

  • Wu J, Rangan S, Zhang H (2016) Green communications: theoretical fundamentals, algorithms, and applications. CRC Press Inc, Boca Raton

    Book  Google Scholar 

  • 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):61–67

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chun-Wei Tsai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsai, CW., Liu, SJ. An effective hyper-dense deployment algorithm via search economics. J Ambient Intell Human Comput 11, 2251–2262 (2020). https://doi.org/10.1007/s12652-019-01353-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01353-8

Keywords

Navigation