Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks
Автор: Nilakshee Rajule, Mithra Venkatesan, Radhika Menon, Anju Kulkarni
Журнал: International Journal of Computer Network and Information Security @ijcnis
Статья в выпуске: 6 vol.15, 2023 года.
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The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.
Green Cellular Networks, Predictive Model, Energy Efficiency, BS Sleep Modes etc
Короткий адрес: https://sciup.org/15018811
IDR: 15018811 | DOI: 10.5815/ijcnis.2023.06.06
Список литературы Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks
- M. Masoudi et al., “Green Mobile Networks for 5G and beyond,” IEEE Access, vol. 7, pp. 107270–107299, 2019, doi: 10.1109/ACCESS.2019.2932777.
- H. Panahi, Fereidoun & H. Panahi, Farzad & Hattab, Ghaith & Ohtsuki, Tomoaki & Cabric, Danijela. (2018). Green Heterogeneous Networks via an Intelligent Sleep/Wake-Up Mechanism and D2D Communications. IEEE Transactions on Green Communications and Networking. PP. 1-1. 10.1109/TGCN.2018.2844301.
- Farooq, Junaid & Ghazzai, Hakim & Yaacoub, E. & Kadri, Abdullah & Alouini, Mohamed-Slim. (2017). Green Virtualization for Multiple Collaborative Cellular Operators. IEEE Transactions on Cognitive Communications and Networking. PP. 10.1109/TCCN.2017.2712133.
- Turgut, Esma & Gursoy, M. Cenk. (2017). Uplink Performance Analysis in D2D-Enabled mmWave Cellular Networks. 10.1109/VTCFall.2017.8288068.
- C. Qiu, Y. Zhang, Z. Feng, P. Zhang, and S. Cui, “Spatio-Temporal Wireless Traffic Prediction with Recurrent Neural Network,” IEEE Wireless Communications Letters, vol. 7, no. 4, pp. 554–557, Aug. 2018, doi: 10.1109/LWC.2018.2795605.
- Institute of Electrical and Electronics Engineers, 2017 IEEE 25th International Conference on Network Protocols (ICNP) : ICNP 2017 : proceedings : 10-13 October 2017, Toronto.
- S. Jaffry, “Cellular Traffic Prediction with Recurrent Neural Network,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.02807
- S. Qiao, R. Sun, G. Fan, and J. Liu, “Short-Term Traffic Flow Forecast Based on Parallel Long Short-Term Memory Neural Network.”
- A. Azari, P. Papapetrou, S. Denic, and G. Peters, “Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11828 LNAI, pp. 129–144. doi: 10.1007/978-3-030-33778-0_11.
- Q. Zhaowei, L. Haitao, L. Zhihui, and Z. Tao, “Short-Term Traffic Flow Forecasting Method with M-B-LSTM Hybrid Network,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 225–235, Jan. 2022, doi: 10.1109/TITS.2020.3009725.
- Q. K. W. L. and L. T. Y. B. Wang, “On efficient utilization of green energy in heterogeneous cellular networks,” B. Wang, Q. Kong, W. Liu, and L. T. Yang.
- J. Wu, Y. Bao, G. Miao, S. Zhou, and Z. Niu, ‘‘Base-station sleeping control and power matching for energy–delay tradeoffs with bursty traffic,’’ IEEE Trans. Veh. Technol., vol. 65, no. 5, pp. 3657–3675, May 2016.
- M. Masoudi, M. G. Khafagy, E. Soroush, D. Giacomelli, S. Morosi and C. Cavdar, "Reinforcement Learning for Traffic-Adaptive Sleep Mode Management in 5G Networks," 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020, pp. 1-6, doi: 10.1109/PIMRC48278.2020.9217286.
- Jing Wu, Yun Li, Hongcheng Zhuang, Zhiwen Pan, Guoyin Wang, Yongju Xian, “SMDP-based sleep policy for base stations in heterogeneous cellular networks”, Digital Communications and Networks", Volume 7, Issue 1, 2021, Pages 120-130, ISSN 2352-8648, https://doi.org/10.1016/j.dcan.2020.04.010.
- G. Jang, N. Kim, T. Ha, C. Lee and S. Cho, "Base Station Switching and Sleep Mode Optimization With LSTM-Based User Prediction," in IEEE Access, vol. 8, pp. 222711-222723, 2020, doi: 10.1109/ACCESS.2020.3044242.
- Post, Bart & Borst, Sem & Berg, Hans. (2021). A self-organizing base station sleeping and user association strategy for dense cellular networks. Wireless Networks. 27. 1-16. 10.1007/s11276-020-02383-3.
- J. J. Q. Yu and V. O. K. Li, "Base station switching problem for green cellular networks with Social Spider Algorithm," 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, pp. 2338-2344, doi: 10.1109/CEC.2014.6900235.
- Kang, M.W.; Chung, Y.W. An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization. Energies 2017, 10,1417. https://doi.org/10.3390/en10091417
