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Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review

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  • Preetjot Kaur

    (Panjab University)

  • Roopali Garg

    (Panjab University)

  • Vinay Kukreja

    (Chitkara University)

Abstract

In today’s 5G era, the energy efficiency (EE) of cellular base stations is crucial for sustainable communication. Recognizing this, Mobile Network Operators are actively prioritizing EE for both network maintenance and environmental stewardship in future cellular networks. The paper aims to provide an outline of energy-efficient solutions for base stations of wireless cellular networks. A total of 5722 studies have been figured out by using the search string and after performing the six stages of SLR protocol, 82 studies were finalised that are published in 26 supreme journals and 19 featured conferences. EE solutions have been segregated into five primary categories: base station hardware components, sleep mode strategies, radio transmission mechanisms, network deployment and planning, and energy harvesting. The predominance of sleep mode procedures is evident in the selected survey studies. Notably, China, Korea, and the US are vigorously engaged in this field, specifically related to the 5G network. This review paper identifies the possible potential solutions for reducing the energy consumption of the networks and discusses the challenges so that more accurate and valid measures could be designed for future research.

Suggested Citation

  • Preetjot Kaur & Roopali Garg & Vinay Kukreja, 2023. "Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 84(1), pages 115-151, September.
    • Handle: RePEc:spr:telsys:v:84:y:2023:i:1:d:10.1007_s11235-023-01037-x
    DOI: 10.1007/s11235-023-01037-x
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    References listed on IDEAS

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    1. Giacomo Peruzzi & Alessandro Pozzebon, 2020. "A Review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs)," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Rohit Kumar Gupta & Saubhik Kumar & Rajiv Misra, 2023. "Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(1), pages 141-159, January.
    3. Lehr, William & Queder, Fabian & Haucap, Justus, 2021. "5G: A new future for Mobile Network Operators, or not?," Telecommunications Policy, Elsevier, vol. 45(3).
    4. Min Wook Kang & Yun Won Chung, 2017. "An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization," Energies, MDPI, vol. 10(9), pages 1-28, September.
    5. Liuwei Huo & Dingde Jiang, 2019. "Stackelberg game-based energy-efficient resource allocation for 5G cellular networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(3), pages 377-388, November.
    6. Byung Moo Lee & Youngok Kim, 2017. "Interference-Aware PAPR Reduction Scheme to Increase the Energy Efficiency of Large-Scale MIMO-OFDM Systems," Energies, MDPI, vol. 10(8), pages 1-16, August.
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