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Machine Learning Based Small Cell ON/OFF for Energy Efficiency in 5G Heterogeneous Networks

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Abstract

Machine Learning (ML) predictions are envisioned to influence the implementation of 5G networks. 5G heterogeneous networks (Hetnets) are characterized by the combined operation of small cells or small base stations and a main base station in every macrocell coverage area of the cellular network. A vital performance indicator is the network energy efficiency that reflects the system power consumption and the user throughput. The small cells being additional hardware, play a key role in increasing the overall power consumed which can be curbed by incorporating a strategy to turn off the appropriate small cells without affecting the quality of service. User throughput prediction is another key engineering task in mobile communications. In this paper, we propose an ML based small cell ON/OFF algorithm in 5G Hetnets to address the challenges of throughput reduction and optimum power consumption in the small cell switching process. The proposed algorithm is executed as follows: The network parameters or predictors that affect the system throughput are carefully identified. Through multiple linear regression, the correlation between the predictors and the throughput is evaluated. Based on this analysis the ML model is trained to predict the throughput as well as the required number of active small cells for any set of input parameters. The largest RSRP (Received Signal Reference Power) criteria select the small cells that are to be kept ON. Simulation results justify an improvement in throughput and energy efficiency of the proposed scheme by 17% as compared to the benchmark schemes.

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Custom code is available with the authors.

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No real time data was used.

References

  1. Soh, Y., Quek, T., & Kountouris, M. (2013). Energy efficient heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications, 31(5), 840–850.

    Article  Google Scholar 

  2. Zhang, X., Zhou, S., Yan, Y., Xing, C., & Wang, J. (2015). Energy efficient sleep mode activation scheme for small cell networks. In Proceedings of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall) (pp. 1–4).

  3. Hong, E. K., Baek, J. Y., Jang, Y. O., Na, J. H., & Kim, K. S. (2018). QoS-guaranteed scheduling for small cell networks. ICT Express, 4(4), 175–180.

    Article  Google Scholar 

  4. 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 

  5. Tung, L-P., Wang, L-C., & Chen, K-S. (2017). An interference-aware small cell on/off mechanism in hyper dense small cell networks. In 2017 International conference on computing, networking and communications (ICNC). IEEE.

  6. Shen, B., Lei, Z., Huang, X., & Chen, Q. (2018). An interference contribution rate based small cells on/off switching algorithm for 5G dense heterogeneous networks. IEEE Access, 6, 29757–29769.

    Article  Google Scholar 

  7. Fourati, H., Maaloul, R., & Chaari, L. (2021). A survey of 5G network systems: Challenges and machine learning approaches. International Journal on Machine Learning & Cybernetics, 12, 385–431. https://doi.org/10.1007/s13042-020-01178-4

    Article  Google Scholar 

  8. Kim, Y-H., Lee, D-Y., Bae, S-H., & Kim, T. Y. (2021). Improving the performance of heterogeneous network systems in machine learning-based 5G mobile communication system. International Journal of Computers, Communications & Control, 16(6).

  9. El Amine, A., Chaiban, J. P., Hassan, H. A., 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, 4310.

    Article  Google Scholar 

  10. Vallero, G., Renga, D., Meo, M., & Marsan, M. A. (2019). Greener RAN operation through machine learning. IEEE Transactions on Network and Service Management, 16, 896.

    Article  Google Scholar 

  11. Miozzo, M., Giupponi, L., Rossi, M., & Dini, P. (2017). Switch-on/off policies for energy harvesting small cells through distributed Q-learning. In 2017 IEEE wireless communications and networking conference workshops (WCNCW).

  12. Abubakar, A. I., Ozturk, M., Hussain, S., & Imran. M. A. (2019). Q-learning assisted energy-aware traffic offloading and cell switching in heterogeneous networks. In 2019 IEEE 24th international workshop on computer aided modeling and design of communication links and networks (CAMAD).

  13. Li, R., Zhao, Z., Chen, X., Palicot, J., & Zhang, H. (2014). Tact: A transfer actor-critic learning framework for energy saving in cellular radio access networks. IEEE Transactions on Wireless Communications, 13, 2000.

    Article  Google Scholar 

  14. Tranmer, M., Murphy, J., Elliot, M., & Pampaka, M. (2020). Multiple linear regression (2nd ed.), Cathie Marsh Institute Working Paper 202001. https://hummedia.manchester.ac.uk/institutes/cmist/archive-publications/working papers / 2020 /2020-1.

  15. Błaszczyszyn, B., Miodrag, J., & Karray, M. K. (2014). How user throughput depends on the traffic demand in large cellular networks. In 2014 12th International symposium on modeling and optimization in mobile, Ad Hoc, and Wireless networks (WiOpt). IEEE.

  16. Park, T.-Y., Han, J.-W., & Hong, E.-K. (2020). UE throughput guaranteed small cell ON/OFF algorithm with machine learning. Journal Of Communications And Networks, 22(3), 223.

    Article  Google Scholar 

  17. Shen, B., Lei, Z., Huang, X., & Chen, Q. (2018). An interference contribution rate based small cells on/off switching algorithm for 5G dense heterogeneous networks. IEEE Access, 6, 29757–29769.

    Article  Google Scholar 

  18. Phaiwitthayaphorn, P., Boonsrimuang, P., Reangsuntea P., Fujii T., Sanada, K., Mori, K., & Kobayashi, H. (2017). Cell throughput based sleep control scheme for heterogeneous cellular networks. In 14th International conference on electrical engineering/electronics, computer, telecommunications and information technology.

  19. Ye, J., Ge, X., Mao, G., & Zhong, Y. (2018). 5g ultra dense networks with nonuniform distributed users. IEEE Transactions on Vehicular Technology, 67(3), 2660–2670.

    Article  Google Scholar 

  20. Alassane, S., Yann, B., Alberto, B., Philippe, D., & Gwendal, S. (2016). Throughput pre-diction in cellular networks: Experiments and preliminary results. Bayonne, France. hal-01311158v2.

  21. Liu, Y., & Lee, J. Y. (2015). An empirical study of throughput prediction in mobile data networks. In 2015 IEEE Global Communications Conference(GLOBECOM) (pp. 1–6). IEEE.

  22. Ruzat, U., Marwat, S. N. K., Ahmad, A. M., Ahmed, S., Hafeez, A., Kamal, T., & Tufail, M. (2020). A machine learning approach for 5G SINR prediction. MDPI Electronics, 9, 1660. https://doi.org/10.3390/electronics9101660

    Article  Google Scholar 

  23. Claussen, H., Lopez-Perez, D., Ho, L., Razavi, R., & Kucera, S. (2017). Small cell networks: Deployment, management, and optimization. John Wiley & Sons.

    Book  Google Scholar 

  24. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.

  25. LTE. (2010). Evolved Universal Terrestrial Radio Access (E-UTRA), Radio Frequency (RF) system scenarios (3GPP TR 36.942 version 10.2.0 Release 10).

  26. Disatnik, D., & Sivan, L. (2016). The multicollinearity illusion in moderated regression analysis. Marketing Letters, 27(2), 403–408.

    Article  Google Scholar 

  27. Oh, E., Son, K., & Krishnamachari, B. (2013). Dynamic base station switching-on/off strategies for green cellular networks. IEEE Transactions on Wireless Communications, 12(5), 2126–2136.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of ECE, National Institute of Technology, Tiruchirappally, India

    Janani Natarajan & B. Rebekka

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  1. Janani Natarajan
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  2. B. Rebekka
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Conceptualization, methodology, software, writing—original draft. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Janani Natarajan.

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Natarajan, J., Rebekka, B. Machine Learning Based Small Cell ON/OFF for Energy Efficiency in 5G Heterogeneous Networks. Wireless Pers Commun 130, 2367–2383 (2023). https://doi.org/10.1007/s11277-023-10383-7

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