IMPROVING ENERGY EFFICIENCY OF 5G RADIO ACCESS NETWORK

Chinedu R. Okpara, Victor E. Idigo, Sophia C. Nwanesindu

Abstract


As evolution of green technology increases, the energy efficiency in wireless networks has become a major focus. 5G networks supports large number of devices and requires high data rates, needing much energy consumption. The need to efficiently manage this energy consumption using machine learning is a major drive for this research. A 5G production dataset was cleaned, normalized and analyzed. Correlation investigation results shows that the highest correlation value of 0.78 exists between the reference signal received power (RSRP) and the reference signal received power of the neighbouring cells (nrxRSRP). A Dynamic Power Control algorithm was formulated and validated. Using the formulated Dynamic Power Control algorithm, the transmission power was seen to reduce from an absolute value of 91.7dBm to an absolute value of 65dBm, which improved on the energy efficiency of 5G network from 4.57 * 1011 Mbps/Watts to 4.45 * 1010 Mbps/Watts.

 

KEYWORDS: 5G, artificial intelligence, energy efficiency, improve, machine learning.


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References


.Boyle, D., Kolcun, R. and Yeatman, E. (2017). “Energy-Efficient Communication in Wireless Networks”. Open Access Peer-reviewed Chapter. DOI: 10.5772/65986 www.intechopen.com/chapters/53177 (2017)

.Yadav, R. (2017). “Challenges and evolution of next generations wireless communication”. In Proc. Int. MultiConf. Eng. Comput. Scientists, vol. 2, pp. 619 – 623 (2017).

.Panwar, N., Sharma, S. & Singh, A. K. (2016). “A Survey on 5G: The next generation of mobile communication”. Phys. Commun., vol. 18, pp. 64-84 (2016).

.Lahdekorpi, P., Hronec, M., Jolma, P. and Moilanen, J. (2017). “Energy efficiency of 5G mobile networks with base station sleep modes”. in Proc. IEEE Conf. Standards for Commun. Netw. (CSCN), pp. 163 -168 (2017).

.Johnson, D. D. (2018). “The 5G Dilemma: More Base Stations, More Antenna-Less Energy?” Available from: https://spectrum.ieee.org/energywise/telecom/wireless/will-increased-energy-consumption-be-the-achilles-heel-of-5g-networks [25th January, 2022] (2018)

.Raca, D., Leahy D., Sreenan C. J. and Quinlan, J. J. (2020). “Beyond Throughput, The Next Generation: A 5G Dataset with Channel and Context Metrics”. ACM Multimedia Systems Conference (MMSys), Turkey. DOI: 10.1145/3339825.3394938 (2020)

.Okpara, C. R., Idigo, V. E. and Okafor, C. S. (2023). “Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach.” EJ-ENG Volume 8, Issue 2, https://www.ej-eng.org/index.php/ejeng/article/view/2994. DOI:10.24018/ejeng.2023.8.2.2994 (2023).

Okpara, C. R., Idigo, V. E. and Ngwu, O. P. (2023), “Improving the Energy Efficiency of a 5G network: The Machine Learning Approach”, Iconic Research and Engineering (IRE) journals, ISSN: 2456-8880, IRE 1704107, Vol. 6, Issue 8, Pp. 142 – 145 (2023).


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