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Binary-PSO-based energy-efficient small cell deployment in 5G ultra-dense network

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Abstract

The energy-efficient deployment of small cells helps to reduce environmental pollution in an ultra-dense network. In contrast, demand for massive connectivity and higher data rate are the promise of the present cellular system and small cell networks. Hence, energy consumption is reduced if base stations are optimally used. One way to improve the energy efficiency is by shutting down the redundant BSs while sustaining the Quality of Service for each user. This paper proposes an efficient cell modeling (ECM) algorithm for small cell formation, and binary particle swarm optimization-based small cell deployment (BPSD) to optimize the deployment of small base stations in the small cell network. The small base stations (s-BSs) exist in two modes: active and sleep which is decided by the proposed algorithm without compromising the network performance. The proposed ECM and BPSD algorithms are implemented and evaluated in MATLAB. The results demonstrate that the proposed approaches improve the energy efficiency and connectivity in the ultra-dense small cell network.

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References

  1. 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 

  2. An J, Yang K, Wu J, Ye N, Guo S, Liao Z (2017) Achieving sustainable ultra-dense heterogeneous networks for 5G. IEEE Commun Mag 55(12):84–90

    Article  Google Scholar 

  3. Baum DS, Hansen J, Salo J, Del Galdo G, Milojevic M, Kyösti P (2005) An interim channel model for beyond-3G systems: extending the 3GPP spatial channel model (SCM). In: 2005 IEEE 61st Vehicular Technology Conference, vol 5. IEEE, pp 3132–3136

  4. Cai X, Liu X, Qu Z (2019) Game theory-based device-to-device network access algorithm for heterogeneous networks. J Supercomput 75(5):2423–2435

    Article  Google Scholar 

  5. Celebi H, Yapıcı Y, Güvenç I, Schulzrinne H (2019) Load-based On/Off scheduling for energy-efficient delay-tolerant 5G networks. IEEE Trans Green Commun Netw 3(4):955–970

    Article  Google Scholar 

  6. Christophorou C, Pitsillides A, Akyildiz I (2017) Celec framework for reconfigurable small cells as part of 5G ultra-dense networks. In: 2017 IEEE International Conference on Communications (ICC). IEEE, pp 1–7

  7. Damodaran SP, Srinivasan VK, Rajakani K (2019) Optimized and low-complexity power allocation and beamforming with full duplex in massive MIMO and small-cell networks. J Supercomput 75(12):7979–7993

    Article  Google Scholar 

  8. de Freitas Bezerra D, Santos GL, Gonçalves G, Moreira A, da Silva LGF, da Silva Rocha É, Marquezini MV, Kelner J, Sadok D, Mehta A et al (2021) Optimizing NFV placement for distributing micro-data centers in cellular networks. J Supercomputing. https://doi.org/10.1007/s11227-021-03620-y

    Article  Google Scholar 

  9. de Oliveira CHR, Costa APF, Thomaz VF, Silva IA (2019) Low-cost deployment proposal to urban mobility in smart cities. J Supercomput 75(11):7265–7289

    Article  Google Scholar 

  10. El-Amine A, Hassan HAH, Iturralde M, Nuaymi L (2019) Location-aware sleep strategy for energy-delay tradeoffs in 5G with reinforcement learning. In: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, pp 1–6

  11. Feng M, Mao S, Jiang T (2017) Base station ON–OFF switching in 5G wireless networks: approaches and challenges. IEEE Wirel Commun 24(4):46–54

    Article  Google Scholar 

  12. Hashim MF, Razak NIA (2019) Ultra-dense networks: integration with device to device (D2D) communication. Wirel Pers Commun 106(2):911–925

    Article  Google Scholar 

  13. Kabalci Y (2019) 5G mobile communication systems: fundamentals, challenges, and key technologies. In: Kabalci E, Kabalci Y (eds) Smart grids and their communication systems. Springer, Berlin, pp 329–359

    Chapter  Google Scholar 

  14. Kang S, Yoon W (2016) SDN-based resource allocation for heterogeneous LTE and WLAN multi-radio networks. J Supercomput 72(4):1342–1362

    Article  Google Scholar 

  15. Kaur K, Kumar S, Baliyan A (2018) 5G: a new era of wireless communication. Int J Inf Technol 12:1–6

    Google Scholar 

  16. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4. IEEE, pp 1942–1948

  17. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol 5. IEEE, pp 4104–4108

  18. Liu C, Natarajan B, Xia H (2015) Small cell base station sleep strategies for energy efficiency. IEEE Trans Vehic Technol 65(3):1652–1661

    Article  Google Scholar 

  19. Mollahasani S, Onur E (2019) Density-aware, energy-and spectrum-efficient small cell scheduling. IEEE Access 7:65852–65869

    Article  Google Scholar 

  20. Swain P, Christophorou C, Bhattacharjee U, Silva CM, Pitsillides A (2018) Selection of UE-based virtual small cell base stations using affinity propagation clustering. In: 2018 14th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, pp 1104–1109

  21. Taşgetiren MF, Liang YC (2003) A binary particle swarm optimization algorithm for loT sizing problem. J Econ Soc Res 5(2):1–20

    Google Scholar 

  22. Vadgama S, Hunukumbure M (2011) Trends in green wireless access networks. In: 2011 IEEE International Conference on Communications Workshops (ICC). IEEE, pp 1–5

  23. Venkateswararao K, Swain P (2020) Traffic aware sleeping strategies for small-cell base station in the ultra dense 5G small cell networks. In: 2020 IEEE Region 10 Conference (TENCON). IEEE, pp 102–107

  24. Venkateswararao K, Swain P, Christophorou C, Pitsillides A (2021) Using UE-VBS for dynamic virtual small cells deployment and backhauling in 5G ultra-dense networks. Comput Netw 189:107926

    Article  Google Scholar 

  25. Wang Y, Zhu X (2019) A novel network planning algorithm of three-dimensional dense networks based on adaptive variable-length particle swarm optimization. IEEE Access 7:45940–45950

    Article  Google Scholar 

  26. Xu Y, Chen J, Wu D, Xu W (2016) Toward 5G: a novel sleeping strategy for green distributed base stations in small cell networks. In: 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). IEEE, pp 115–119

  27. Xu Y, Yang P, Gong J, Niu K (2018) A self-organizing base station sleeping strategy in small cell networks using local stable matching games. In: International Conference on Wireless Algorithms, Systems, and Applications. Springer, Berlin, pp 545–556

  28. Zhang T, Zhao J, An L, Liu D (2016) Energy efficiency of base station deployment in ultra dense HetNets: a stochastic geometry analysis. IEEE Wirel Commun Lett 5(2):184–187

    Article  Google Scholar 

  29. Zhao L, Zhang X, Han Y, Shin KG (2018) Power saving with CoMP transmission for densely deployed small cell networks. J Supercomput 76:1–19

    Google Scholar 

  30. Zhou L, Hu X, Zhu C, Ngai ECH, Wang S, Wei J, Leung VC (2015) Green small cell planning in smart cities under dynamic traffic demand. In: 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, pp 618–623

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Goa, Goa, 403401, India

    Kuna Venkateswararao & Pravati Swain

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  1. Kuna Venkateswararao
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  2. Pravati Swain
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Correspondence to Kuna Venkateswararao.

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Venkateswararao, K., Swain, P. Binary-PSO-based energy-efficient small cell deployment in 5G ultra-dense network. J Supercomput 78, 1071–1092 (2022). https://doi.org/10.1007/s11227-021-03910-5

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