Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

An Adaptive Cell Switch Off framework to Increase Energy Efficiency in Cellular Networks

  • Research
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Energy efficiency is regarded as a critical concern in modern cellular networks, primarily due to the increasing demand for wireless communication services and the environmental impact associated with energy consumption. In this paper, an innovative approach named ACSO (Adaptive Cell Switch Off) is proposed to optimize energy efficiency in cellular networks. It focusses on leveraging clustering techniques and dynamic CSO (Cell Switch Off) strategies. In this approach, clustering of BSs (Base Station) is performed based on their traffic profiles, and within each cluster, an optimal set of BSs is determined to be switched off at different time intervals. By considering the characteristics of network traffic and spatial distribution, an attempt is made to strike a balance between energy conservation and the maintenance of high QoE (Quality of Experience) for users. Extensive simulations using a real-world dataset are conducted to compare our approach with existing methods, and superior performance is demonstrated in terms of efficient BS selection, algorithm execution time, and user QoE. Additionally, potential future research directions in the area of dynamic CSO are discussed, along with its implications for the development of energy-efficient cellular networks.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Availability of data and materials

For simulations, a real-world mobile base station traffic dataset from Milan city and the Province of Trentino in Italy was utilized, as provided in [75].

Notes

  1. Quality of Service.

  2. Mobile Cellular Network.

  3. Macro Base Station.

  4. Heterogeneous Networks.

  5. User Equipment.

  6. Energy Efficiency.

  7. Dense Cellular Network.

  8. Femto Base Stations.

  9. Semi-Markov Decision Process.

  10. Unmanned aerial vehicles Base Station.

  11. Small Cell Network.

  12. Triangular Lattice.

  13. Poisson Point Process.

  14. Repulsive Point Process.

  15. Macro Base Station.

  16. Signal-to-Interference-plus-Noise Ratio.

  17. Heterogeneous Cellular Network.

  18. Poisson Tessellation Limit.

  19. Signal-to-Interference Ratio.

  20. User Equipment.

  21. Advanced Sleep Modes.

  22. Mobile Network Operator.

  23. Gateway Core Network.

  24. Radio Access Network.

  25. Energy-Harvesting-Aided.

  26. Full-Duplex.

  27. Millimeter Wave.

  28. Small Cell.

  29. Backhaul.

  30. Software-Defined Networking.

  31. Acess Point.

  32. Multiple-Input and Multiple-Output.

  33. Discontinuous Reception.

  34. Cloud Radio Access Network.

  35. Virtual Base Station.

  36. Remote Radio Head.

  37. BaseBand Unit.

  38. Radio Resource Unit.

  39. Convolutional Neural Network.

  40. Deep Q-Network.

  41. Virtual Machine.

  42. Hierarchical Cloud Radio Access Network.

  43. Deep Reinforcement Learning.

  44. Ultra-Dense Networks.

  45. deep neural network.

  46. long-short term memory.

  47. multi-graph convolutional network.

  48. Artificial Neural Networks.

  49. Software Defined Radio.

  50. Energy Efficiency.

  51. Spectral Efficiency.

  52. Highest Reference Signal Receive Power.

  53. Triangular Lattice.

References

  1. Ericsson. (2022). Ericsson Mobility Report. Ericsson (no. November, p. 40). Retrieved from, www.ericsson.com/mobility-report

  2. Farnworth, E., & Castilla-rubio, J. C. (2008). SMART 2020: Enabling the low carbon economy in the information age (p. 45). The Climate Group.

    Google Scholar 

  3. Post, B., Borst, S., & van den Berg, H. (2020). A self-organizing base station sleeping and user association strategy for dense cellular networks. Wireless Networks, 3(1), 307–322. https://doi.org/10.1007/s11276-020-02383-3

    Article  Google Scholar 

  4. Han, F., Zhao, S., Zhang, L., & Wu, J. (2016). Survey of strategies for switching off base stations in heterogeneous networks for greener 5G systems. IEEE Access, 4(c), 4959–4973. https://doi.org/10.1109/ACCESS.2016.2598813

    Article  Google Scholar 

  5. Salahdine, F., Opadere, J., Liu, Q., Han, T., Zhang, N., & Wu, S. (2021). A survey on sleep mode techniques for ultra-dense networks in 5G and beyond. Computer Networks, 201, 108567. https://doi.org/10.1016/j.comnet.2021.108567

    Article  Google Scholar 

  6. Alamu, O., Gbenga-Ilori, A., Adelabu, M., Imoize, A., & Ladipo, O. (2020). Energy efficiency techniques in ultra-dense wireless heterogeneous networks: An overview and outlook. Engineering Science and Technology, an International Journal, 23(6), 1308–1326.

    Article  Google Scholar 

  7. Niu, Z., Wu, Y., Gong, J., & Yang, Z. (2010). Cell zooming for cost-efficient green cellular networks. IEEE Communications Magazine, 48(11), 74–79. https://doi.org/10.1109/MCOM.2010.5621970

    Article  Google Scholar 

  8. Liu, Z., Chen, X., Yang, Y., Chan, K. Y., & Yuan, Y. (2023). Joint cell zooming and sleeping strategy in ultra dense heterogeneous networks. Computer Networks, 220, 109482.

    Article  Google Scholar 

  9. Gonzalez, D. G., Hamalainen, J., Yanikomeroglu, H., Garcia-Lozano, M., & Senarath, G. (2016). A novel multiobjective cell switch-off framework for cellular networks. IEEE Access, 4(Xx), 7883–7898. https://doi.org/10.1109/ACCESS.2016.2625743

    Article  Google Scholar 

  10. Dutta, U. K., Razzaque, M. A., Abdullah Al-Wadud, M., Islam, M. S., Shamim Hossain, M., & Gupta, B. B. (2018). Self-adaptive scheduling of base transceiver stations in green 5G networks. IEEE Access, 6, 7958–7969. https://doi.org/10.1109/ACCESS.2018.2799603

    Article  Google Scholar 

  11. Lee, W., Jung, B. C., & Lee, H. (2020). DeCoNet: Density clustering-based base station control for energy-efficient cellular IoT networks. IEEE Access, 8, 120881–120891.

    Article  Google Scholar 

  12. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., & Pan, J.-S. (2020). Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access, 8, 32935–32946.

    Article  Google Scholar 

  13. Habibi, S., Solouk, V., & Kalbkhani, H. (2021). Adaptive energy-efficient small cell sleeping and zooming in heterogeneous cellular networks. Telecommunication Systems, 77, 23–45. https://doi.org/10.1007/s11235-020-00740-3

    Article  Google Scholar 

  14. Sidiq, S., Sheikh, J. A., Mustafa, F., & Malik, B. A. (2022). A new method of hybrid optimization of small cell range development and density for energy efficient ultra-dense networks. Transactions on Emerging Telecommunications Technologies, 33(7), e4476.

    Article  Google Scholar 

  15. Ben Rached, N., Member, S., Ghazzai, H., & Kadri, A. (2018). A time-varied probabilistic ON/OFF switching algorithm for cellular networks a time-varied probabilistic ON/OFF switching algorithm for cellular networks. IEEE Communications Letters. https://doi.org/10.1109/LCOMM.2018.2792001

    Article  Google Scholar 

  16. Dahmani, S., Gabli, M., Mermri, E. B., & Serghini, A. (2020). Optimization of green RNP problem for LTE networks using possibility theory. Neural Computing and Applications, 32, 3825–3838.

    Article  Google Scholar 

  17. Wu, J., Li, Y., Zhuang, H., Pan, Z., Wang, G., & Xian, Y. (2021). SMDP-based sleep policy for base stations in heterogeneous cellular networks. Digital Communications and Networks, 7(1), 120–130.

    Article  Google Scholar 

  18. Chang, W., Meng, Z.-T., Liu, K.-C., & Wang, L.-C. (2021). Energy-efficient sleep strategy for the UBS-assisted small-cell network. IEEE Transactions on Vehicular Technology, 70(5), 5178–5183.

    Article  Google Scholar 

  19. Jahid, A., Alsharif, M. H., Uthansakul, P., Nebhen, J., & Aly, A. A. (2021). Energy efficient throughput aware traffic load balancing in green cellular networks. IEEE Access, 9, 90587–90602.

    Article  Google Scholar 

  20. Soh, Y. S., Quek, T. Q. S., Kountouris, M., & Shin, H. (2013). Energy efficient heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications, 31(5), 840–850. https://doi.org/10.1109/JSAC.2013.130503

    Article  Google Scholar 

  21. Liu, Q. (2018). Base station sleep and spectrum allocation in heterogeneous ultra-dense networks. Wireless Personal Communications, 98, 3611–3627. https://doi.org/10.1007/s11277-017-5031-4

    Article  Google Scholar 

  22. Altman, E., et al. (2015). Stochastic geometric models for green networking. IEEE Access, 3, 2465–2474. https://doi.org/10.1109/ACCESS.2015.2503322

    Article  Google Scholar 

  23. Liu, C., Natarajan, B., & Xia, H. (2015). Small cell base station sleep strategies for energy efficiency. IEEE Transactions on Vehicular Technology, 65(3), 1652–1661. https://doi.org/10.1109/TVT.2015.2413382

    Article  Google Scholar 

  24. Lagum, F., Le-The, Q.-N., Beitelmal, T., Szyszkowicz, S. S., & Yanikomeroglu, H. (2017). Cell switch-off for networks deployed with variable spatial regularity. IEEE Wireless Communications Letters, 6(2), 234–237.

    Article  Google Scholar 

  25. Chen, J., Ge, X., Song, X., & Zhong, Y. (2017). Base station switch-off with mutual repulsion in 5G massive MIMO networks. IET Communications, 12(16), 1–9.

    Google Scholar 

  26. Yu, G., Chen, Q., & Yin, R. (2014). Dual-threshold sleep mode control scheme for small cells. IET Communications, 8(11), 2008–2016. https://doi.org/10.1049/iet-com.2013.0831

    Article  Google Scholar 

  27. Wang, H., Huang, M., Zhao, Z., Guo, Z., Wang, Z., & Li, M. (2020). Base station wake-up strategy in cellular networks with hybrid energy supplies for 6G networks in an IoT environment. IEEE Internet of Things Journal, 8(7), 5230–5239.

    Article  Google Scholar 

  28. Niu, Z., Guo, X., Zhou, S., & Kumar, P. R. (2015). Characterizing energy–delay tradeoff in hyper-cellular networks with base station sleeping control. IEEE Journal on Selected Areas in Communications, 33(4), 641–650.

    Article  Google Scholar 

  29. Guo, X., Niu, Z., Zhou, S., & Kumar, P. R. (2016). Delay-constrained energy-optimal base station sleeping control. IEEE Journal on Selected Areas in Communications, 34(5), 1073–1085.

    Article  Google Scholar 

  30. Wu, J., Wong, E. W. M., Chan, Y. C., & Zukerman, M. (2020). Power consumption and GoS tradeoff in cellular mobile networks with base station sleeping and related performance studies. IEEE Transactions on Green Communications and Networking, 4(4), 1024–1036. https://doi.org/10.1109/TGCN.2020.3000277

    Article  Google Scholar 

  31. Renga, D., Umar, Z., & Meo, M. (2023). Trading off delay and energy saving through advanced sleep modes in 5G RANs. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2023.3248291

    Article  Google Scholar 

  32. Dong, W., et al. (2013). iDEAL: Incentivized dynamic cellular offloading via auctions. IEEE/ACM Transactions on Networking, 22(4), 1271–1284.

    Article  Google Scholar 

  33. Bousia, A., et al. (2016). Multiobjective auction-based switching-off scheme in heterogeneous networks to bid or not to bid. IEEE Transactions on Vehicular Technology, 65(11), 9168–9180. https://doi.org/10.1109/TVT.2016.2517698

    Article  Google Scholar 

  34. Ghazzai, H., Yaacoub, E., & Member, S. (2016). Next-generation environment-aware cellular networks: Modern green techniques and implementation challenges. IEEE Access, 4, 5010–5029. https://doi.org/10.1109/ACCESS.2016.2609459

    Article  Google Scholar 

  35. Hossain, F., Munasinghe, K. S., & Jamalipour, A. (2019). Energy-efficient inter-RAN cooperation for non-collocated cell sites with base station selection and user association policies. Wireless Networks, 25, 269–285. https://doi.org/10.1007/s11276-017-1556-4

    Article  Google Scholar 

  36. Feng, M., Mao, S., & Jiang, T. (2017). BOOST: Base station on-off switching strategy for green massive MIMO HetNets. IEEE Transactions on Wireless Communications, 16(11), 7319–7332. https://doi.org/10.1109/TWC.2017.2746689

    Article  Google Scholar 

  37. Cheng, Y., Zhang, J., Zhang, J., Zhao, H., Yang, L., & Zhu, H. (2021). Small-cell sleeping and association for energy-harvesting-aided cellular IoT with full-duplex self-backhauls: A game-theoretic approach. IEEE Internet of Things Journal, 9(3), 2304–2318.

    Article  Google Scholar 

  38. Alqasir, A. M., & Kamal, A. E. (2020). Cooperative small cell HetNets with dynamic sleeping and energy harvesting. IEEE Transactions on Green Communications and Networking, 4(3), 774–782.

    Article  Google Scholar 

  39. López-Pérez, D., et al. (2022). A survey on 5G radio access network energy efficiency: Massive MIMO, lean carrier design, sleep modes, and machine learning. IEEE Communications Surveys & Tutorials, 24(1), 653–697.

    Article  Google Scholar 

  40. Mesodiakaki, A., Zola, E., Santos, R., & Kassler, A. (2018). Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links. Ad Hoc Networks, 78, 99–114. https://doi.org/10.1016/j.adhoc.2018.05.008

    Article  Google Scholar 

  41. García-Morales, J., Femenias, G., & Riera-Palou, F. (2020). Energy-efficient access-point sleep-mode techniques for cell-free mmWave massive MIMO networks with non-uniform spatial traffic density. IEEE Access, 8, 137587–137605.

    Article  Google Scholar 

  42. Ostrikova, D., Beschastnyi, V., Moltchanov, D., Gaidamaka, Y., Koucheryavy, Y., & Samouylov, K. (2023). System-level analysis of energy and performance trade-offs in mmWave 5G NR systems. IEEE Transactions on Wireless Communications, 22(11), 7304–7318.

    Article  Google Scholar 

  43. Van Chien, T., Björnson, E., & Larsson, E. G. (2020). Joint power allocation and load balancing optimization for energy-efficient cell-free massive MIMO networks. IEEE Transactions on Wireless Communications, 19(10), 6798–6812.

    Article  Google Scholar 

  44. Alnoman, A., & Anpalagan, A. S. (2019). Computing-aware base station sleeping mechanism in H-CRAN-cloud-edge networks. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/tcc.2019.2893228

    Article  Google Scholar 

  45. Sun, G., Ayepah-Mensah, D., Xu, R., Boateng, G. O., & Liu, G. (2020). End-to-end CNN-based dueling deep Q-Network for autonomous cell activation in Cloud-RANs. Journal of Network and Computer Applications, 169, 102757.

    Article  Google Scholar 

  46. Hajisami, A., Tran, T. X., Younis, A., & Pompili, D. (2020). Elastic resource provisioning for increased energy efficiency and resource utilization in Cloud-RANs. Computer Networks, 172, 107170.

    Article  Google Scholar 

  47. Sigwele, T., Hu, Y. F., & Susanto, M. (2020). Energy-efficient 5G cloud RAN with virtual BBU server consolidation and base station sleeping. Computer Networks, 177(February), 107302. https://doi.org/10.1016/j.comnet.2020.107302

    Article  Google Scholar 

  48. Fowdur, T. P., & Doorgakant, B. (2023). A review of machine learning techniques for enhanced energy efficient 5G and 6G communications. Engineering Applications of Artificial Intelligence, 122, 106032. https://doi.org/10.1016/j.engappai.2023.106032

    Article  Google Scholar 

  49. Liu, J., Krishnamachari, B., Zhou, S., & Niu, Z. (2018). DeepNap: Data-driven base station sleeping operations through deep reinforcement learning. IEEE Internet of Things Journal, 5(6), 4273–4282. https://doi.org/10.1109/JIOT.2018.2846694

    Article  Google Scholar 

  50. Ye, J., & Zhang, Y. J. (2019). DRAG: Deep reinforcement learning based base station activation in heterogeneous networks. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/tmc.2019.2922602

    Article  Google Scholar 

  51. Wu, Q., Chen, X., Zhou, Z., Chen, L., & Zhang, J. (2021). Deep reinforcement learning with spatio-temporal traffic forecasting for data-driven base station sleep control. IEEE/ACM Transactions on Networking, 29(2), 935–948.

    Article  Google Scholar 

  52. Ju, H., Kim, S., Kim, Y., & Shim, B. (2022). Energy-efficient ultra-dense network with deep reinforcement learning. IEEE Transactions on Wireless Communications, 21(8), 6539–6552.

    Article  Google Scholar 

  53. El Amine, A., Chaiban, J.-P., Hassan, H. A. H., Dini, P., Nuaymi, L., & Achkar, R. (2022). Energy optimization with multi-sleeping control in 5G heterogeneous networks using reinforcement learning. IEEE Transactions on Network and Service Management, 19(4), 4310–4322.

    Article  Google Scholar 

  54. Lee, H., Kim, E., Kim, H., Na, J., & Choi, H.-H. (2022). Multi-agent Q-learning based cell breathing considering SBS collaboration for maximizing energy efficiency in B5G heterogeneous networks. ICT Express, 8(4), 525–529.

    Article  Google Scholar 

  55. Malta, S., Pinto, P., & Fernández-Veiga, M. (2023). Using reinforcement learning to reduce energy consumption of ultra-dense networks with 5G use cases requirements. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3236980

    Article  Google Scholar 

  56. Lee, W., Lee, H., & Choi, H.-H. (2023). Deep learning-based network-wide energy efficiency optimization in ultra-dense small cell networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2023.3237551

    Article  Google Scholar 

  57. Jang, G., Kim, N., Ha, T., Lee, C., & Cho, S. (2020). Base station switching and sleep mode optimization with lstm-based user prediction. IEEE Access, 8, 222711–222723.

    Article  Google Scholar 

  58. Lin, J., Chen, Y., Zheng, H., Ding, M., Cheng, P., & Hanzo, L. (2021). A data-driven base station sleeping strategy based on traffic prediction. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2021.3109614

    Article  Google Scholar 

  59. Premsankar, G., Piao, G., Nicholson, P. K., Di Francesco, M., & Lugones, D. (2021). Data-driven energy conservation in cellular networks: A systems approach. IEEE Transactions on Network and Service Management, 18(3), 3567–3582.

    Article  Google Scholar 

  60. Shinkuma, R., Kishi, N., Ota, K., Dong, M., Sato, T., & Oki, E. (2021). Smarter base station sleeping for greener cellular networks. IEEE Network, 35(6), 98–103.

    Article  Google Scholar 

  61. Zhu, Y., & Wang, S. (2022). Traffic prediction enabled dynamic access points switching for energy saving in dense networks. Digital Communications and Networks, 9, 1023–1031.

    Article  Google Scholar 

  62. Vallero, G., Renga, D., Meo, M., & Marsan, M. A. (2022). RAN energy efficiency and failure rate through ANN traffic predictions processing. Computer Communications, 183, 51–63.

    Article  Google Scholar 

  63. Mohajer, A., Daliri, M. S., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2022). Heterogeneous computational resource allocation for NOMA: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing, 16(2), 1225–1238.

    Article  Google Scholar 

  64. Dong, S., Zhan, J., Hu, W., Mohajer, A., Bavaghar, M., & Mirzaei, A. (2023). Energy-efficient hierarchical resource allocation in uplink–downlink decoupled NOMA HetNets. IEEE Transactions on Network and Service Management, 20, 3380–3395.

    Article  Google Scholar 

  65. Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16(4), 5188–5199.

    Article  Google Scholar 

  66. Kumar, A., & Reddy, B. (2019). Interference and QoS aware cell switch-off strategy for software defined LTE HetNets. Journal of Network and Computer Applications, 125(May 2018), 115–129. https://doi.org/10.1016/j.jnca.2018.10.006

    Article  Google Scholar 

  67. Xu, F., Li, Y., Member, S., Wang, H., Zhang, P., & Jin, D. (2017). Understanding mobile traffic patterns of large scale cellular towers in urban environment. IEEE/ACM Transactions on Networking, 25(2), 1147–1161.

    Article  Google Scholar 

  68. Lagum, F., & Affairs, P. (2018). Stochastic geometry-based tools for spatial modeling and planning of future cellular networks: Opportunistic cell switch-off and strategic deployment of flying base stations.

  69. Pisinger, D. (2000). Exact solution of p-dispersion problems. Citeseer.

    Google Scholar 

  70. Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning. STHDA.

    Google Scholar 

  71. Abanda, A., Mori, U., & Lozano, J. A. (2019). A review on distance based time series classification. Data Mining and Knowledge Discovery, 33(2), 378–412. https://doi.org/10.1007/s10618-018-0596-4

    Article  MathSciNet  Google Scholar 

  72. Shevlyakov, G. L., & Oja, H. (2016). Robust correlation: Theory and applications. Wiley.

    Book  Google Scholar 

  73. Keogh, E. J., & Pazzani, M. J. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In Proceedings of the fourth international conference on knowledge discovery and data mining (pp. 239–243). Retrieved from, http://dl.acm.org/citation.cfm?id=3000292.3000335

  74. Haslwanter, T. (2021). Hands-on signal analysis with Python: An introduction. Springer International Publishing.

    Book  Google Scholar 

  75. Barlacchi, G., et al. (2015). A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Scientific Data, 2(1), 150055. https://doi.org/10.1038/sdata.2015.55

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Seyed Mohsen Safavi & Seyed Amin Hosseini Seno

  2. Department of Computing and Information Technology, Sohar University, Sohar, Oman

    Amirhossein Mohajerzadeh

Authors
  1. Seyed Mohsen Safavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Amin Hosseini Seno
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amirhossein Mohajerzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mohsen participates in writing all sections. Amir participates in sections 1, 2, 3, 4 and 5. Amin helped revising the whole paper.

Corresponding author

Correspondence to Seyed Amin Hosseini Seno.

Ethics declarations

Competing Interests

The authors have not disclosed any competing interests.

Ethical approval

We are dedicated to ensuring that our project remains in compliance with the approved ethical standards.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safavi, S.M., Seno, S.A.H. & Mohajerzadeh, A. An Adaptive Cell Switch Off framework to Increase Energy Efficiency in Cellular Networks. Wireless Pers Commun 135, 2011–2037 (2024). https://doi.org/10.1007/s11277-024-11027-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11027-0

Keywords

Search

Navigation