Skip to main content
Log in

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

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

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.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Auer, G., et al. (2011). How much energy is needed to run a wireless network? IEEE Wireless Communications, 18(5), 40–49. https://doi.org/10.1109/MWC.2011.6056691

    Article  Google Scholar 

  2. 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 

  3. Mohamed, K. S., Alias, M. Y., Roslee, M., & Raji, Y. M. (2021). Towards green communication in 5G systems: Survey on beamforming concept. IET Communications, 15(1), 142–154. https://doi.org/10.1049/cmu2.12066

    Article  Google Scholar 

  4. Lehr, W., Queder, F., & Haucap, J. (2021). 5G: A new future for mobile network operators, or not? Telecommunications Policy, 45(3), 102086. https://doi.org/10.1016/j.telpol.2020.102086

    Article  Google Scholar 

  5. Kuklinski, S., & Tomaszewski, L. (2019). Key performance indicators for 5G network slicing. In: 2019 IEEE conference on network softwarization (NetSoft), Jun. 2019, pp. 464-471. https://doi.org/10.1109/NETSOFT.2019.8806692.

  6. Soos, G., Ficzere, D., Varga, P., & Szalay, Z. (2020). Practical 5G KPI measurement results on a non-standalone architecture. In: NOMS 2020—2020 IEEE/IFIP network operations and management symposium, Apr. 2020, pp. 1-5. https://doi.org/10.1109/NOMS47738.2020.9110457.

  7. De Ree, M., Mantas, G., Radwan, A., Mumtaz, S., Rodriguez, J., & Otung, I. E. (2019). Key management for beyond 5G mobile small cells: A survey. IEEE Access, 7, 59200–59236. https://doi.org/10.1109/ACCESS.2019.2914359

    Article  Google Scholar 

  8. Yan, J., Zhou, M., & Ding, Z. (2016). Recent advances in energy-efficient routing protocols for wireless sensor networks: A review. IEEE Access, 4, 5673–5686. https://doi.org/10.1109/ACCESS.2016.2598719

    Article  Google Scholar 

  9. Oh, E., Krishnamachari, B., Liu, X., & Niu, Z. (2011). Toward dynamic energy-efficient operation of cellular network infrastructure. IEEE Communications Magazine, 49(6), 56–61. https://doi.org/10.1109/MCOM.2011.5783985

    Article  Google Scholar 

  10. I, C.-L., Han, S., & Bian, S. (2020). Energy-efficient 5G for a greener future. Nature Electronics, 3, no. 4, pp. 182–184, Apr. https://doi.org/10.1038/s41928-020-0404-1

  11. Sakshi and V. Kukreja. (2021). A retrospective study on handwritten mathematical symbols and expressions: Classification and recognition. Engineering Applications of Artificial Intelligence, 103, 104292. https://doi.org/10.1016/j.engappai.2021.104292

  12. Kitchenham, B., et al. (2010). Systematic literature reviews in software engineering—A tertiary study. Information and Software Technology, 52(8), 792–805. https://doi.org/10.1016/j.infsof.2010.03.006

    Article  Google Scholar 

  13. Barbara Kitchenham, S.C. (2007). Guidelines for performing systematic literature reviews in software engineering, [Online]. Available: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf

  14. ITU. (2018). Enlisting technologies in the fight against climate change, ITU News.

  15. Hasan, Z., Boostanimehr, H., & Bhargava, V. K. (2011). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys and Tutorials, 13(4), 524–540. https://doi.org/10.1109/SURV.2011.092311.00031

    Article  Google Scholar 

  16. Son, K., Kim, H., Yi, Y., & Krishnamachari, B. (2011). Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks. IEEE Journal on Selected Areas in Communications, 29(8), 1525–1536. https://doi.org/10.1109/JSAC.2011.110903

    Article  Google Scholar 

  17. Zappone, A., Bjornson, E., Sanguinetti, L., & Jorswieck, E. (2017). globally optimal energy-efficient power control and receiver design in wireless networks. IEEE Transactions on Signal Processing, 65(11), 2844–2859. https://doi.org/10.1109/TSP.2017.2673813

    Article  Google Scholar 

  18. Miracco, T.: (2008). Crest factor reduction and digital pre-distortion for wireless RF power amplifier optimization. In: 2008 9th International conference on solid-state and integrated-circuit technology, Oct. pp. 1357–1360. https://doi.org/10.1109/ICSICT.2008.4734813

  19. J. S. & P. B. Erik Dahlman, Stefan Parkvall, (2009). 3G radio access evolution—HSPA and LTE for mobile broadband. IEICE Transactions on Communications, 92B(5), pp. 1432–1440, https://doi.org/10.1587/transcom.E92.B.1432

  20. G. Auer et al., Cellular energy efficiency evaluation framework. In: 2011 IEEE 73rd vehicular technology conference (VTC Spring), May 2011, pp. 1–6. https://doi.org/10.1109/VETECS.2011.5956750.

  21. 3GPP TR 36.814 v2.0.1 (2010). Evolved universal terrestrial radio access (E-UTRA); Further advancements for E-UTRA physical layer aspects. Tech. Spec. Group Radio Access Net.

  22. Salman, M. I., Ng, C. K., & Noordin, N. K. (2012). Energy- and Spectral-Efficient Wireless Cellular Networks, pp. 171–185.

  23. Holtkamp, H., Auer, G., Bazzi, S., & Haas, H. (2014). Minimizing base station power consumption. IEEE Journal on Selected Areas in Communications, 32(2), 297–306. https://doi.org/10.1109/JSAC.2014.141210

    Article  Google Scholar 

  24. Oliver Arnold, O. B., Richter, F., Fettweis, G., (2010). Power consumption modeling of different base station types in heterogeneous cellular networks.

  25. Keller, T., & Hanzo, L. (2000). Adaptive modulation techniques for duplex OFDM transmission. IEEE Transactions on Vehicular Technology, 49(5), 1893–1906. https://doi.org/10.1109/25.892592

    Article  Google Scholar 

  26. Devi, R. P. & Prabakaran, (2021). Hybrid cuckoo search with salp swarm optimization for spectral and energy efficiency maximization in NOMA system. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-09361-8

  27. Tan, W., Li, S., & Zhou, M. (2022). Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture. Physical Communication, 50, 101516. https://doi.org/10.1016/j.phycom.2021.101516

    Article  Google Scholar 

  28. Gupta, M., Jha, S. C., Koc, A. T., & Vannithamby, R. (2013). Energy impact of emerging mobile internet applications on LTE networks: Issues and solutions. IEEE Communications Magazine, 51(2), 90–97. https://doi.org/10.1109/MCOM.2013.6461191

    Article  Google Scholar 

  29. Zhang, Y. & Årvidsson, A. (2012). Understanding the characteristics of cellular data traffic. In: Proceedings of the 2012 ACM SIGCOMM workshop on Cellular networks: Operations, challenges, and future design—CellNet ’12, p. 13. https://doi.org/10.1145/2342468.2342472

  30. Dufkova, K., Bjelica, M., Moon, B., Kencl, L., & Le Boudec, J.-Y. (2010). Energy savings for cellular network with evaluation of impact on data traffic performance. In: 2010 European Wireless Conference (EW), Apr. pp. 916–923. https://doi.org/10.1109/EW.2010.5483431

  31. Elayoubi, S.-E., Saker, L., & Chahed, T. (2011). Optimal control for base station sleep mode in energy efficient radio access networks. In: 2011 Proceedings IEEE INFOCOM, Apr. pp. 106–110. https://doi.org/10.1109/INFCOM.2011.5934881

  32. Samal, S. R. (2018). Interference management techniques in small cells overlaid heterogeneous cellular networks. Journal of Mobile Multimedia, 14(3), 273–306. https://doi.org/10.13052/jmm1550-4646.1432

    Article  Google Scholar 

  33. Baccelli, S. Z. F., Klein, M., Lebourges, M., Lebourges, M., Zuyev, S., (1997). Stochastic geometry and architecture of communication networks. Telecommunication Systems, 7(1). https://doi.org/10.1023/A:1019172312328.

  34. Foss, S. G., & Zuyev, S. A. (1996). On a Voronoi aggregative process related to a bivariate Poisson process. Advances in Applied Probability, 28(4), 965–981. https://doi.org/10.2307/1428159

    Article  Google Scholar 

  35. Damnjanovic, A., et al. (2011). A survey on 3GPP heterogeneous networks. IEEE Wireless Communications, 18(3), 10–21. https://doi.org/10.1109/MWC.2011.5876496

    Article  Google Scholar 

  36. Gruber, M., Blume, O., Ferling, D., Zeller, D., Imran, M. A., & Strinati, E. C. (2009). EARTH : Energy aware radio and network technologies. In: 2009 IEEE 20th international symposium on personal, indoor and mobile radio communications, Sep. pp. 1–5. https://doi.org/10.1109/PIMRC.2009.5449938.

  37. Radwan, A., Rodriguez, J., Gomes, A., & Sa, E. (2012) C2POWER approach for power saving in multi-standard wireless devices, pp. 440–451. https://doi.org/10.1007/978-3-642-35155-6_35

  38. GreenTouch. (2013). GreenTouch green meter research study: Reducing the net energy consumption in communications networks by up to 90% by (2020). A GreenTouch White Paper, no. Version, 1.

  39. Atiyah Abd, A., Sieh Kiong, T., Koh, J., Chieng, D., & Ting, A. (2012). Energy efficiency of heterogeneous cellular networks: A review. Journal of Applied Sciences, 12, no. 14, pp. 1418–1431. Jul. https://doi.org/10.3923/jas.2012.1418.1431.

  40. Zoie, R. C., Delia Mihaela, R., & Alexandru, S. (2017). An analysis of the power usage effectiveness metric in data centers. In: 2017 5th International symposium on electrical and electronics engineering (ISEEE), Oct. 2017, pp. 1–6. https://doi.org/10.1109/ISEEE.2017.8170650

  41. Badic, B., O’Farrrell, T., Loskot, P., & He J. (2009) Energy efficient radio access architectures for green radio: large versus small cell size deployment. In: 2009 IEEE 70th vehicular technology conference fall, Sep. 2009, pp. 1–5. https://doi.org/10.1109/VETECF.2009.5379035

  42. He, C., Sheng, B., Zhu, P., & You, X. (2012). Energy efficiency and spectral efficiency tradeoff in downlink distributed antenna systems. IEEE Wireless Communications Letters, 1(3), 153–156. https://doi.org/10.1109/WCL.2012.022812.120048

    Article  Google Scholar 

  43. Mao, H., Zhu, P., & Li, J. (2018). Energy consumption index minimized resource allocation in hybrid energy multiuser OFDM system with distributed antennas. ITM Web of Conferences, 17, 03015. https://doi.org/10.1051/itmconf/20181703015

    Article  Google Scholar 

  44. Richter, F., Fehske, A. J., & Fettweis, G. P. (2009). Energy efficiency aspects of base station deployment strategies for cellular networks, In: 2009 IEEE 70th vehicular technology conference fall, Sep. pp. 1–5. https://doi.org/10.1109/VETECF.2009.5379031

  45. Badic, B., O’Farrrell, T., Loskot, P., & He, J. (2009). Energy efficient radio access architectures for green radio: Large versus small cell size deployment. In: 2009 IEEE 70th vehicular technology conference fall, Sep. 2009, pp. 1–5. https://doi.org/10.1109/VETECF.2009.5379035

  46. Tabassum, H., Shakir, M. Z., & Alouini, M.-S. (2012). Area green efficiency (AGE) of two tier heterogeneous cellular n., In: 2012 IEEE Globecom workshops, Dec. 2012, pp. 529–534. https://doi.org/10.1109/GLOCOMW.2012.6477629

  47. Salh, A., et al. (2022). Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G. IEEE Access, 10, 4714–4727. https://doi.org/10.1109/ACCESS.2021.3139338

    Article  Google Scholar 

  48. Saraiva, J. V. (2021). Energy efficiency maximization under minimum rate constraints in multi-cell MIMO systems with finite buffers. IEEE Transactions on Green Communications and Networking, 5(1), 174–189. https://doi.org/10.1109/TGCN.2020.3043049

    Article  Google Scholar 

  49. Kolawole, O. Y., Biswas, S., Singh, K., & Ratnarajah, T. (2020). Transceiver design for energy-efficiency maximization in mmWave MIMO IoT networks. IEEE Transactions on Green Communications and Networking, 4(1), 109–123. https://doi.org/10.1109/TGCN.2019.2943956

    Article  Google Scholar 

  50. Pavel, B., Matousek, D., & Rejfek, L. (2019). Nonlinear distortion in a microwave high power amplifier. In: 2019 29th International conference radioelektronika (RADIOELEKTRONIKA), Apr. 2019, pp. 1–4. https://doi.org/10.1109/RADIOELEK.2019.8733505

  51. Bjornson, E., & Larsson, E. G. (2018). How energy-efficient can a wireless communication system become? In: 2018 52nd Asilomar conference on signals, systems, and computers, Oct. 2018, pp. 1252–1256, https://doi.org/10.1109/ACSSC.2018.8645227

  52. Moghadam, N. N., Fodor, G., Bengtsson, M., & Love, D. J. (2018). On the energy efficiency of MIMO hybrid beamforming for millimeter wave systems with nonlinear power amplifiers, Jun. [Online]. Available: arxiv:1806.01602.

  53. Younis, A., Tran, T. X., & Pompili, D. (2018). Bandwidth and energy-aware resource allocation for cloud radio access networks. IEEE Transactions on Wireless Communications, 17(10), 6487–6500. https://doi.org/10.1109/TWC.2018.2860008

    Article  Google Scholar 

  54. Devi, R. V. S., & Kurup, D. G. (2017). Behavioral modeling of RF power amplifiers for designing energy efficient wireless systems. In: 2017 International conference on wireless communications, signal processing and networking (WiSPNET), Mar. 2017, pp. 1994–1998. https://doi.org/10.1109/WiSPNET.2017.8300110

  55. Lee, B., & Kim, Y. (2017). Interference-aware PAPR reduction scheme to increase the energy efficiency of large-scale MIMO-OFDM systems. Energies, 10(8), 1184. https://doi.org/10.3390/en10081184

    Article  Google Scholar 

  56. IEEE Computer society. (2006). IEEE standard for local and metropolitan area networks part 16: Air interface for fixed and mobile broadband wireless access systems amendment 2: Physical and medium access control layers for combined fixed and mobile operation in licensed bands and Corri, (2006). [Online]. Available: https://people.cs.clemson.edu/~jmarty/projects/WiMAX/802.16e-2005.pdf

  57. ETSI, LTE (2011). Evolved Universal Terrestrial Radio Access (E-UTRA); Potential solutions for energy saving for E-UTRAN, 3GPP TR 36.927 version 10.0.0 Release 10).

  58. Vereecken, W. et al, (2012). The effect of variable wake up time on the utilization of sleep modes in femtocell mobile access networks. In: 2012 9th Annual conference on wireless on-demand network systems and services (WONS), Jan. 2012, pp. 63–66. https://doi.org/10.1109/WONS.2012.6152239

  59. Piovesan, N., Lopez-Perez, D., Miozzo, M., & Dini, P. (2021). Joint load control and energy sharing for renewable powered small base stations: A machine learning approach. IEEE Transactions on Green Communications and Networking, 5(1), 512–525. https://doi.org/10.1109/TGCN.2020.3027063

    Article  Google Scholar 

  60. Mathonsi, T. E., & Tshilongamulenzhe, T. M. (2020). Intelligent energy efficiency algorithm for the 5G dense heterogeneous cellular networks. International Conference on Computational Science and Computational Intelligence (CSCI), 2020, 144–149. https://doi.org/10.1109/CSCI51800.2020.00032

    Article  Google Scholar 

  61. Hossain, Md. S., et al. (2020). Towards energy efficient load balancing for sustainable green wireless networks under optimal power supply. IEEE Access, 8, 200635–200654. https://doi.org/10.1109/ACCESS.2020.3035447

    Article  Google Scholar 

  62. Veerappan Kousik, N. G., Natarajan, Y., Suresh, K., Patan, R., & Gandomi, A. H. (2020). Improving power and resource management in heterogeneous downlink ofdma networks. Information, 11, no. 4, p. 203, Apr. , https://doi.org/10.3390/info11040203

  63. Ashtari, S., Tofigh, F., Abolhasan, M., Lipman, J., & Ni, W. (2019). Efficient cellular base stations sleep mode control using image matching. In: 2019 IEEE 89th Vehicular technology conference (VTC2019-Spring), Apr. 2019, pp. 1–7. https://doi.org/10.1109/VTCSpring.2019.8746343

  64. Wang, Y., Dai, X., Wang, J. M., & Bensaou, B. (2019). A reinforcement learning approach to energy efficiency and QoS in 5G wireless networks. IEEE Journal on Selected Areas in Communications, 37(6), 1413–1423. https://doi.org/10.1109/JSAC.2019.2904365

    Article  Google Scholar 

  65. Ramamoorthi, Y., & Kumar, A. (2018). Resource allocation for CoMP in cellular networks with base station sleeping. IEEE Access, 6, 12620–12633. https://doi.org/10.1109/ACCESS.2017.2783398

    Article  Google Scholar 

  66. [75] Arani, A. H., Omidi, M. J., Mehbodniya, A., & Adachi, F. (2018). A distributed satisfactory sleep mode scheme for self-organizing heterogeneous networks. In: Iranian conference on electrical engineering (ICEE), May 2018, pp. 476–481. https://doi.org/10.1109/ICEE.2018.8472421.

  67. Herrería-Alonso, S., Rodríguez-Pérez, M., Fernández-Veiga, M., & López-García, C. (2018). An optimal dynamic sleeping control policy for single base stations in green cellular networks. Journal of Network and Computer Applications, 116, 86–94. https://doi.org/10.1016/j.jnca.2018.05.014

    Article  Google Scholar 

  68. Kang & Chung, Y. (2017). An efficient energy saving scheme for base stations in 5G networks with separated data and control planes using particle swarm optimization. Energies, 10, no. 9, p. 1417, Sep. https://doi.org/10.3390/en10091417

  69. Celebi, H., & Guvenc, I. (2017). Load analysis and sleep mode optimization for energy-efficient 5G small cell networks. In: 2017 IEEE international conference on communications workshops (ICC Workshops), May, pp. 1159–1164. https://doi.org/10.1109/ICCW.2017.7962815

  70. Sylla, T., Mendiboure, L., Maaloul, S., Aniss, H., Chalouf, M. A., & Delbruel, S. (2022). Multi-connectivity for 5G networks and beyond: A survey. Sensors, 22(19), 7591. https://doi.org/10.3390/s22197591

    Article  Google Scholar 

  71. Alotaibi, S. (2022). Key challenges of mobility management and handover process In 5G HetNets. International Journal of Computer Science and Network Security, 22(4), 139–146.

    Google Scholar 

  72. Jong, C., Kim, J.-H., Pak, C.-S., Nam, C.-M., & Ri, J.-H. (2022). A study on the resource block allocation method to enhance the total energy efficiency for LTE-A networks. Wireless Personal Communications, 123(3), 2679–2697. https://doi.org/10.1007/s11277-021-09260-y

    Article  Google Scholar 

  73. Koolivand, M., Bahonar, M. H., & Fazel, M. S. (2019). Improving energy efficiency of massive MIMO relay systems using power bisection allocation for cell-edge users. In: 2019 27th Iranian conference on electrical engineering (ICEE), Apr. 2091, pp. 1470–1475. https://doi.org/10.1109/IranianCEE.2019.8786368

  74. Huo, L., & Jiang, D. (2019). Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommunication Systems, 72(3), 377–388. https://doi.org/10.1007/s11235-019-00564-w

    Article  Google Scholar 

  75. Song, X., Dong, L., Wang, J., Qin, L., & Han, X. (2019). Energy efficient power allocation for downlink NOMA heterogeneous networks with imperfect CSI. IEEE Access, 7, 39329–39340. https://doi.org/10.1109/ACCESS.2019.2906780

    Article  Google Scholar 

  76. Park, H., & Lim, Y. (2018). Energy-effective power control algorithm with mobility prediction for 5G heterogeneous cloud radio access network. Sensors, 18(9), 2904. https://doi.org/10.3390/s18092904

    Article  Google Scholar 

  77. Lashgari, M., Maham, B., & Kebriaei, H. (2018). Energy efficient price based power allocation in a small cell network by using a stackelberg game. In: 2018 IEEE international black sea conference on communications and networking (BlackSeaCom), Jun. 2018, pp. 1–5. https://doi.org/10.1109/BlackSeaCom.2018.8433625.

  78. Gao, D., Liang, Z., Zhang, H., Dobre, O. A., & Karagiannidis, G. K. (2018). Stackelberg game-based energy efficient power allocation for heterogeneous NOMA networks. In: 2018 IEEE global communications conference (GLOBECOM), Dec. 2018, pp. 1–5. https://doi.org/10.1109/GLOCOM.2018.8647786

  79. Zhang, H., Fang, F., Cheng, J., Long, K., Wang, W., & Leung, V. C. M. (2018). Energy-efficient resource allocation in NOMA heterogeneous networks. IEEE Wireless Communications, 25(2), 48–53. https://doi.org/10.1109/MWC.2018.1700074

    Article  Google Scholar 

  80. Ashraf, M., & Lee, K.-G.: (2017). On the power allocation of base station with energy efficient relay cooperation. In: Proceedings of the 6th international conference on informatics, environment, energy and applications, Mar. 2017, pp. 85–88, https://doi.org/10.1145/3070617.3070630

  81. Trichopoulos, G. C., et al. (2022). Design and evaluation of reconfigurable intelligent surfaces in real-world environment. IEEE Open Journal of the Communications Society, 3, 462–474. https://doi.org/10.1109/OJCOMS.2022.3158310

    Article  Google Scholar 

  82. Huang, C., Zappone, A., Alexandropoulos, G. C., Debbah, M., & Yuen, C. (2019). Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Transactions on Wireless Communications, 18(8), 4157–4170. https://doi.org/10.1109/TWC.2019.2922609

    Article  Google Scholar 

  83. Amponis, G., et al. (2022). Drones in B5G/6G networks as flying base stations. Drones, 6(2), 39. https://doi.org/10.3390/drones6020039

    Article  Google Scholar 

  84. Rohit, V., Hampika, G., Tenneti, A., & Guduri, M. (2020). An architectural overview of unmanned aerial vehicle with 5G technology, pp. 325–330.

  85. Amorosi, L., Chiaraviglio, L., D’Andreagiovanni, F., & Blefari-Melazzi, N. (2018). Energy-efficient mission planning of UAVs for 5G coverage in rural zones. In: 2018 IEEE international conference on environmental engineering (EE), Mar. 218, pp. 1–9. https://doi.org/10.1109/EE1.2018.8385250

  86. Gupta, R. K., Kumar, S., & Misra, R. (2023). Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning. Telecommunication Systems, 82(1), 141–159. https://doi.org/10.1007/s11235-022-00974-3

    Article  Google Scholar 

  87. Alfaia, R. D., Souto, A. V. de F., Cardoso, E. H. S., de Araújo, J. P. L., & Francês, C. R. L.(2022). Resource management in 5G networks assisted by UAV base stations: Machine learning for overloaded macrocell prediction based on users’ temporal and spatial flow, drones, 6, no. 6, p. 145, Jun. https://doi.org/10.3390/drones6060145

  88. Sobouti, M. J., Mohajerzadeh, A. H., Seno, S. A. H., & Yanikomeroglu, H. (2022) Managing sets of flying base stations using energy efficient 3D trajectory planning in cellular networks, Feb. [Online]. Available: arxiv:2202.03834.

  89. Salehi, S., & Eslamnour, B. (2021). Improving UAV base station energy efficiency for industrial IoT URLLC services by irregular repetition slotted-ALOHA. Computer Networks, 199, 108415. https://doi.org/10.1016/j.comnet.2021.108415

    Article  Google Scholar 

  90. French, A., Mozaffari, M., Eldosouky, A., Saad, W. (2019). Environment-aware deployment of wireless drones base stations with google earth simulator. In: 2019 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), Mar. 2019, pp. 868–873. https://doi.org/10.1109/PERCOMW.2019.8730845

  91. Becvar, Z., Mach, P., Plachy, J., & de Tudela, M. F. P. (2019). Positioning of flying base stations to optimize throughput and energy consumption of mobile devices. In: 2019 IEEE 89th vehicular technology conference (VTC2019-Spring), Apr. 219, pp. 1–7. https://doi.org/10.1109/VTCSpring.2019.8746555.

  92. Fotouhi, A., Ding, M., & Hassan, M. (2018). Flying drone base stations for macro hotspots. IEEE Access, 6, 19530–19539. https://doi.org/10.1109/ACCESS.2018.2817799

    Article  Google Scholar 

  93. Peesapati, S. K. G., Olsson, M., Masoudi, M., Andersson, S., & Cavdar, C. (2021) An analytical energy performance evaluation methodology for 5G base stations. In: 2021 17th International conference on wireless and mobile computing, networking and communications (WiMob), pp. 169–174. https://doi.org/10.1109/WiMob52687.2021.9606296.

  94. Bashar, M., et al. (2021). Uplink spectral and energy efficiency of cell-free massive MIMO With optimal uniform quantization. IEEE Transactions on Communications, 69(1), 223–245. https://doi.org/10.1109/TCOMM.2020.3028305

    Article  Google Scholar 

  95. 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), pp. 102–107. https://doi.org/10.1109/TENCON50793.2020.9293754

  96. Dastoor, S. K., Dalal, U., & Sarvaiya, J. (2019). Cellular planning for next generation wireless mobile network using novel energy efficient CoMP. Cluster Computing, 22, no. S2, pp. 4611–4623, Mar. 2019. https://doi.org/10.1007/s10586-018-2229-5.

  97. Chehri, A., & Jeon, G. (2018) Optimal matching between energy saving and traffic load for mobile multimedia communication. Concurrency and Computation: Practice and Experience, 33, no. 4. https://doi.org/10.1002/cpe.5035

  98. Demirtas, M., & Soysal, A. (2017). Nonoverlay heterogeneous network planning for energy efficiency. Wireless Communications and Mobile Computing, 2017, 1–11. https://doi.org/10.1155/2017/6519709

    Article  Google Scholar 

  99. Fan, C., Zhang, T., Zeng, Z. (2017). Energy-efficient base station deployment in HetNet based on traffic load distribution. In: 2017 IEEE 85th vehicular technology conference (VTC Spring), Jun. 2017, pp. 1–5. https://doi.org/10.1109/VTCSpring.2017.8108475

  100. Pizzo, A., Verenzuela, D., Sanguinetti, L., & Björnson, E. (2017). Network Deployment for Maximal Energy Efficiency in Uplink with Multislope Path Loss. https://doi.org/10.1109/TGCN.2018.2839346

  101. Abubakar, A. I., Mollel, M. S., Ozturk, M., Hussain, S., & Imran, M. A. (2022). A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks. Journal of Physics Communications, 52, 101643. https://doi.org/10.1016/j.phycom.2022.101643

    Article  Google Scholar 

  102. Mir, U. (2020). Joint uplink and downlink power allocation for maximizing the energy efficiency in ultra-dense networks. International Journal of Information Technology. https://doi.org/10.1007/s41870-020-00510-z

    Article  Google Scholar 

  103. Nguyen, H. T., et al. (2020). Joint user association and power allocation for millimeter-wave ultra-dense networks. Mobile Networks and Applications, 25(1), 274–284. https://doi.org/10.1007/s11036-019-01286-8

    Article  Google Scholar 

  104. Zhu, Q., Wang, X., & Qian, Z. (2019). Energy-efficient small cell cooperation in ultra-dense heterogeneous networks. IEEE Communications Letters, 23(9), 1648–1651. https://doi.org/10.1109/LCOMM.2019.2926705

    Article  Google Scholar 

  105. Peng, J., Zeng, J., Su, X., Liu, B., & Zhao, H. (2019). A QoS-based cross-tier cooperation resource allocation scheme over ultra-dense HetNets. IEEE Access, 7, 27086–27096. https://doi.org/10.1109/ACCESS.2019.2901506

    Article  Google Scholar 

  106. Zhang, G., Zhang, H., Han, Z., & Karagiannidis, G. K. (2019). Spectrum allocation and power control in full-duplex ultra-dense heterogeneous networks. IEEE Transactions on Communications, 67(6), 4365–4380. https://doi.org/10.1109/TCOMM.2019.2897765

    Article  Google Scholar 

  107. Luo, Y., Shi, Z., Bu, F., & Xiong, J. (2019). Joint optimization of area spectral efficiency and energy efficiency for two-tier heterogeneous ultra-dense networks. IEEE Access, 7, 12073–12086. https://doi.org/10.1109/ACCESS.2019.2891551

    Article  Google Scholar 

  108. Chen, X., Wu, X., Han, S., & Xie, Z. (2019). Joint optimization of EE and SE considering interference threshold in ultra-dense networks. In: 2019 15th International wireless communications and mobile computing conference (IWCMC), Jun. 219, pp. 1305–1310. https://doi.org/10.1109/IWCMC.2019.8766581

  109. Lei, J., Chen, H., & Zhao, F. (2018). Stochastic geometry analysis of downlink spectral and energy efficiency in ultradense heterogeneous cellular networks. Mobile Information Systems, 2018, pp. 1–10.

  110. Chen, Y., Wen, X., Lu, Z., Shao, H., & Jing, W. (2017). Cooperation-enabled energy efficient base station management for dense small cell networks. Wireless Networks, 23(5), 1611–1628. https://doi.org/10.1007/s11276-016-1234-y

    Article  Google Scholar 

  111. Prasad, K. N. R. S. V., Hossain, E., & Bhargava, V. K. (2017). Energy efficiency in massive MIMO-based 5G networks: Opportunities and challenges. IEEE Wireless Communications, 24(3), 86–94. https://doi.org/10.1109/MWC.2016.1500374WC

    Article  Google Scholar 

  112. Verenzuela, D., Björnson, E., & Sanguinetti, L. (2017). Spectral and energy efficiency of superimposed pilots in uplink massive MIMO, Sep. [Online]. Available: arxiv:1709.07722.

  113. Hoffmann, M., Kryszkiewicz, P., & Kliks, A., Increasing energy efficiency of Massive-MIMO network via base stations switching using reinforcement learning and radio environment maps. Computer Communications, 169, 232–242. https://doi.org/10.1016/j.comcom.2021.01.012.

  114. Van Chien, T., Bjornson, E., & Larsson, E. G. (2020). Joint power allocation and load balancing optimization for energy-efficient cell-free massive MIMO networks. IEEE Transactions on Wireless Communication, 19(10), 6798–6812. https://doi.org/10.1109/TWC.2020.3006083

    Article  Google Scholar 

  115. Nimmagadda, S. M. (2020). Optimal spectral and energy efficiency trade-off for massive MIMO technology: analysis on modified lion and grey wolf optimization. Soft Computing, 24(16), 12523–12539. https://doi.org/10.1007/s00500-020-04690-5

    Article  Google Scholar 

  116. Ardah, K., Fodor, G., Silva, Y. C. B., Freitas, W. C., & de Almeida, A. L. F. (2020). Hybrid analog-digital beamforming design for SE and EE maximization in massive MIMO networks. IEEE Transactions on Vehicular Technology, 69(1), 377–389. https://doi.org/10.1109/TVT.2019.2933305

    Article  Google Scholar 

  117. Chen, J.-C. (2020). Low-cost and power-efficient massive MIMO precoding: Architecture and algorithm designs. IEEE Transactions on Vehicular Technology, 69(7), 7429–7442. https://doi.org/10.1109/TVT.2020.2992252

    Article  Google Scholar 

  118. Liu, Y., Feng, Q., Wu, Q., Zhang, Y., Jin, M., & Qiu, T. (2019). Energy-efficient hybrid precoding with low complexity for mmWave massive MIMO systems. IEEE Access, 7, 95021–95032. https://doi.org/10.1109/ACCESS.2019.2928559

    Article  Google Scholar 

  119. Liu, T. (2019). Energy-effcient massive MIMO systems for 5G wireless communication. Doctor of Philosophy thesis, School of Electrical, Computer and Telecommunications Engineering. University of Wollongong. https://ro.uow.edu.au/theses1/724

  120. Ghosh, S., De, D., & Deb, P. (2019). Energy and spectrum optimization for 5G massive MIMO cognitive femtocell based mobile network using auction game theory. Wireless Personal Communications, 106(2), 555–576. https://doi.org/10.1007/s11277-019-06179-3

    Article  Google Scholar 

  121. Zhang, S., et al. (2018). Energy efficient massive MIMO through distributed precoder design. Dec. 2018 [Online]. Available: arxiv:1812.10015.

  122. Matalatala, M., Deruyck, M. Tanghe, E., Martens, L., & Joseph, W. (2018). Optimal low-power design of a multicell multiuser massive MIMO system at 3.7 GHz for 5G wireless networks. Wireless Communications and Mobile Computing, 2018, pp. 1–17, Oct. 2018. https://doi.org/10.1155/2018/9796784

  123. Tan, W., Xie, D., Xia, J., Tan, W., Fan, L., & Jin, S. (2018). Spectral and energy efficiency of massive MIMO for hybrid architectures based on phase shifters. IEEE Access, 6, 11751–11759. https://doi.org/10.1109/ACCESS.2018.2796571

    Article  Google Scholar 

  124. Ribeiro, L. N., Schwarz, S., Rupp, M., & de Almeida, A. L. F. (2018). Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs. IEEE Journal of Selected Topics in Signal Processing, 12(2), 298–312. https://doi.org/10.1109/JSTSP.2018.2824762

    Article  Google Scholar 

  125. Vallero, G., Deruyck, M., Meo, M., & Joseph, W., . Base Station switching and edge caching optimisation in high energy-efficiency wireless access network. Computer Networks, 192, 108100. https://doi.org/10.1016/j.comnet.2021.108100.

  126. Sun, Y., Wei, T., Li, H., Zhang, Y., & Wu, W. (2020). Energy-efficient multimedia task assignment and computing offloading for mobile edge computing networks. IEEE Access, 8, 36702–36713. https://doi.org/10.1109/ACCESS.2020.2973359

    Article  Google Scholar 

  127. Wang, Q., Tan, L. T., Hu, R. Q., & Qian, Y. (2020). Hierarchical energy efficient mobile edge computing in IoT networks. IEEE Internet of Things Journal, pp. 1–1. https://doi.org/10.1109/JIOT.2020.3000193.

  128. Wu, G., Miao, Y., Zhang, Y., & Barnawi, A. (2020). Energy efficient for UAV-enabled mobile edge computing networks: Intelligent task prediction and offloading. Computer Communications, 150, 556–562. https://doi.org/10.1016/j.comcom.2019.11.037

    Article  Google Scholar 

  129. Yang, Z., Pan, C., Hou, J., & Shikh-Bahaei, M. (2019). Efficient resource allocation for mobile-edge computing networks with NOMA: Completion time and energy minimization. IEEE Transactions on Communications, 67(11), 7771–7784. https://doi.org/10.1109/TCOMM.2019.2935717

    Article  Google Scholar 

  130. Sun, H., Zhou, F., & Hu, R. Q. (2019). Joint offloading and computation energy efficiency maximization in a mobile edge computing system. IEEE Transactions on Vehicular Technology, pp. 1–1, https://doi.org/10.1109/TVT.2019.2893094.

  131. Yan et al, M. (2019). Assessing the Energy Consumption of 5G Wireless Edge Caching, in 2019 IEEE International Conference on Communications Workshops (ICC Workshops), May pp. 1-6. https://doi.org/10.1109/ICCW.2019.8756642

  132. Hao, Y., Chen, M., Hu, L., Hossain, M. S., & Ghoneim, A. (2018). Energy efficient task caching and offloading for mobile edge computing. IEEE Access, 6, 11365–11373. https://doi.org/10.1109/ACCESS.2018.2805798

    Article  Google Scholar 

  133. Chiaraviglio, L., et al. (2021). Multi-area throughput and energy optimization of UAV-aided cellular networks powered by solar panels and grid. IEEE Transactions on Mobile Computing, 20(7), 2427–2444. https://doi.org/10.1109/TMC.2020.2980834

    Article  Google Scholar 

  134. Krauss, R., Brante, G., Rayel, O. K., Souza, R. D., Onireti, O., & Imran, M. A. (2019). Energy efficiency of multiple antenna cellular networks considering a realistic power consumption model. The IEEE Transactions on Green Communications and Networking, 3(1), 1–10. https://doi.org/10.1109/TGCN.2018.2868505

    Article  Google Scholar 

  135. Peruzzi, G., & Pozzebon, A. (2020). A review of energy harvesting techniques for low power wide area networks (LPWANs). Energies, 13(13), 3433. https://doi.org/10.3390/en13133433

    Article  Google Scholar 

  136. Chen, H., Li, Y., Luiz Rebelatto, J., Uchoa-Filho, B. F. & B. Vucetic, Harvest-then-cooperate: Wireless-powered cooperative communications. In: IEEE transactions on signal processing, 63, no. 7, pp. 1700-1711, Apr. 2015. https://doi.org/10.1109/TSP.2015.2396009.

  137. Wang, Q., Zhao, F., & Chen, T. (2018). A Base station DTX scheme for OFDMA cellular networks powered by the smart grid. IEEE Access, pp. 1–1. https://doi.org/10.1109/ACCESS.2018.2876395.

  138. Zhang, Z., Qu, H., Zhao, J. & Wang, W. (2020). Deep reinforcement learning method for energy efficient resource allocation in next generation wireless networks. In: Proceedings of the 2020 international conference on computing, networks and internet of things, Apr. 2020, pp. 18–24. https://doi.org/10.1145/3398329.3398332.

  139. Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947. https://doi.org/10.1109/JSAC.2017.2720898

    Article  Google Scholar 

  140. Zhai, D., Zhang, R., Du, J., Ding, Z., & Yu, F. R. (2019). Simultaneous wireless information and power transfer at 5G new frequencies: Channel measurement and network design. IEEE Journal on Selected Areas in Communications, 37(1), 171–186. https://doi.org/10.1109/JSAC.2018.2872366

    Article  Google Scholar 

  141. Akbar, S., Deng, Y., Nallanathan, A., Elkashlan, M., & Aghvami, A.-H. (2016). Simultaneous wireless information and power transfer in K—Tier heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 15(8), 5804–5818. https://doi.org/10.1109/TWC.2016.2570209

    Article  Google Scholar 

  142. Shi, W., Meng, Y., & Gu, L. (2021). A resource allocation scheme of D2D energy harvesting networks based on stochastic learning. In: 2021 IEEE Asia conference on information engineering (ACIE), pp. 6–10. https://doi.org/10.1109/ACIE51979.2021.9381077.

  143. Perez, D. E., Lopez, O. L. A., Alves, H., & Latva-aho, M. (2021). Self-energy recycling for low-power reliable networks: Half-duplex or full-duplex? IEEE System Journal, pp. 1–12. https://doi.org/10.1109/JSYST.2021.3127266

  144. Xu, Y., Xie, H., Liang, C., & Yu, F. R. (2021). Robust secure energy-efficiency optimization in SWIPT-aided heterogeneous networks with a nonlinear energy-harvesting model. IEEE Internet Things Journal, 8(19), 14908–14919. https://doi.org/10.1109/JIOT.2021.3072965

    Article  Google Scholar 

  145. Omidkar, A., Khalili, A., Nguyen, H. H., & Shafiei, H. (2021). Reinforcement learning based resource allocation for energy-harvesting-aided D2D communications in IoT networks. IEEE Internet Things Journal, pp. 1–1. https://doi.org/10.1109/JIOT.2022.3151001

  146. Slovaca. (2021). Energy-aware caching and collaboration for green communication systems. Acta Montan, 26, 47–59. https://doi.org/10.46544/AMS.v26i1.04

  147. Ahmed, F., Naeem, M., Ejaz, W., Iqbal, M., Anpalagan, A., & Haneef, M. (2021). Energy cooperation with sleep mechanism in renewable energy assisted cellular HetNets. Wireless Personal Communications, 116(1), 105–124. https://doi.org/10.1007/s11277-020-07707-2

    Article  Google Scholar 

  148. Zhang, Z., Qu, H., Zhao, J., & Wang, W. (2020). Deep reinforcement learning method for energy efficient resource allocation in next generation wireless networks. In: Proceedings of the 2020 international conference on computing, networks and internet of things, Apr. 2020, pp. 18–24. https://doi.org/10.1145/3398329.3398332

  149. Hossain, M. S., Jahid, A., Islam, K. Z., & Rahman, M. F. (2020). Solar PV and biomass resources-based sustainable energy supply for off-grid cellular base stations. IEEE Access, 8, 53817–53840. https://doi.org/10.1109/ACCESS.2020.2978121

  150. Lee, G., Jung, M., Kasgari, A. T. Z., Saad, W., & Bennis, M. (2020). Deep reinforcement learning for energy-efficient networking with reconfigurable intelligent surfaces in ICC 2020—2020 IEEE international conference on communications (ICC), Jun. 2020, pp. 1–6. https://doi.org/10.1109/ICC40277.2020.9149380

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vinay Kukreja.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Appendices

Appendix A: Acronyms

3GPP

Third Generation Partnership Project

5G

Fifth Generation

AMF

Access and Mobility Management Function

BS

Base Station

C2POWER

Cognitive radio and Cooperative strategies for POWER saving

CAPEX

Capital expenditures

CoMP

Coordinated Multi-Point transmission and reception

CR

Cognitive Radio

DL

DownLink

EARTH

Energy Aware Radio and neTwork tecHnologies

EE

Energy Efficiency

GHG

Greenhouse gas emissions

HetNet

Heterogenous Network

ICT

Information and Communications Technology

IIOT

Industrial Internet of Things

mMIMO

massive Multiple-Input Multiple-Output

MNO

Mobile Network Operators

MU-MC

Multi user and Multi cell

OFDM

Orthogonal frequency division multiplexing

OPEX

Operating Expenses

PA

Power Amplifier

PAPR

Peak to average power ratio

PPP

Poisson Point Process

PSO

Particle Swarm Optimization

QA

Question Answers

QAC

Quality Assurance Criteria

QAM

Quadrature Amplitude Modulation

QoS

Quality of service

QPSK

Quadrature Phase Shift Keying

RAN

Radio Access Network

RAT

Radio access technology

RQ

Research Question

SE

Spectral Efficiency

SLR

Systematic Literature Review

SWIPT

Simultaneous Wireless Information and Power Transfer

UAV

Unmanned Aerial Vehicle

UDN

UltraDense Networks

UE

User Equipment

UL

UpLink

URLLC

Ultra-reliable Low Latency Communications

Appendix B: Quality assessment criteria

1.1 Appendix B.1: Quality assessment framework

Section 1

Screening Question

Does the research paper mention about EE problem of wireless networks? Does the paper specifically refer to the energy consumption problem of base stations?

After evaluating Sect. 1, proceed to Sect. 2 if you obtain a positive response; otherwise, reject the research.

Section 2

Analysis of EE improvement techniques

Does the study depict the technique used for EE of base stations? How well does that approach work, and can it be classified? Has the study mentioned the type of EE technique employed or the details of the algorithms used in the research process?

If the above elements are well-explained, then proceed on to Sect. 3.

Sections 3 and 4

Findings

Is there a concise summary of the findings? Was the experiment’s result shown in the study? Is there enough data for the comparative analysis?

Accept the study if the aforementioned findings are adequately stated; otherwise, reject it.

Section 5

Accuracy Analysis

Is there a discussion over the critical accuracy measures used? Verify if the study is evaluated for accuracy measurement as per the metrics defined in the paper.

Accept it if the aforementioned questions are successfully addressed; otherwise, reject it.

1.2 Appendix B.2: Quality assessment flow

The flowchart represents the flow of questions asked for checking the quality of extracted studies.

figure d

Appendix C: Main highlights of study

The table represents some of the glimpses of the review paper.

Key point statements

Associated facts

The year wherein the first thought of research on EE of wireless networks got initiated

Was 1992 by J. C. Kelly

The year from which the research on this topic speeded up

2010 onwards

The motivation behind the interest of scholars in this field

High cost and environmental concern

The active continent in this research

Asia

Active countries in this research

China and US

Active researcher

Emil Björnson

Major project

EARTH

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

Kaur, P., Garg, R. & Kukreja, V. Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review. Telecommun Syst 84, 115–151 (2023). https://doi.org/10.1007/s11235-023-01037-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-023-01037-x

Keywords

Navigation