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

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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.

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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.

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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

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

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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 (2024). https://doi.org/10.1007/s11277-024-11027-0

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