Interference Mitigation and Power Consumption Reduction for Cell Edge users in Future Generation Networks
Christopher O. Uloh *
Department of Electrical / Electronics Engineering, Akwa Ibom State University, Nigeria.
Emmanuel A. Ubom
Department of Electrical / Electronics Engineering, Akwa Ibom State University, Nigeria.
Akaniyene U. Obot
Department of Electrical / Electronics Engineering, University of Uyo, Nigeria.
Ubong S. Ukommi
Department of Electrical / Electronics Engineering, Akwa Ibom State University, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
In 5G heterogeneous networks (HetNets), a unique and promising option to address the growing demand for higher data rates is network densification of small cells (SCs) and macro cells (MCs). Unfortunately, the 5G HetNets are suffering severe issues due to the interference caused by these densely populated SCs and their high-power consumption. To lessen interference and boost network throughput, a New Soft Frequency Reuse (NSFR) technique is put forth in this work. The proposed scheme uses the Soft Frequency Reuse (SFR) for on/off switching of the SCs according to their Interference Contribution Rate (ICR) values. By splitting the cell region into edge and center zones, it resolves the interference issue caused by the densely packed SCs. Moreover, SC on/off switching addresses the issue of excessive power consumption and improves the 5G network's power efficiency. Furthermore, this work tackles the irregular shape nature problem of 5G HetNets and compares two different proposed shapes for the centre zone of the SC, existing irregular and proposed circular shapes. Additionally, the optimum radius of the centre zone, which maximizes the total system data rate, is obtained. A comparative analysis of power consumption, data rate and power efficiency was performed between the NSFR model, the SFR model and the proposed model. The results show that for 1000 number of equipment, the proposed model has a low power consumption of 1.72KW compared to 3.51KW for SFR and 3.73KW for NSFR. Data rate of 12.19kbps compared to 11.42kbps for SFR and 11.09kbps for NSFR. Also, power efficiency of 610kbps/W compared to 572kbps/W for SFR and 560kbps/W for NSFR. These results imply that the interference mitigation handled by the proposed scheme improves by approximately 22%.
Keywords: 5G cellular network, small cells, macro cells, SFR model, NSFR model
How to Cite
Downloads
References
Fourati H, Maaloul R, Chaari L. A survey of 5G network systems, challenges and machine learning approaches, International Journal of Machine Learning and Cybernetics. 2021;385-431.
Oloyede S, Ozuomba C, Kalu. Shibuya method for computing ten knife edge diffraction loss, software engineering. 2017;5(2):38-43.
Simeon O. Analysis of effective transmission range based on hata model for wireless sensor networks in the C-Band and Ku-Band. Journal of Multidisciplinary Engineering Science and Technology. 2020;7(12): 13673-13679.
Dee Ree M, Mantas G. A. Radwan, S. Mumtaz, J. Rodriguez and I. Otung, Key Management for Beyond 5G Mobile Small Cells: A Survey, IEEE Access. 2019; (7):59200-59236.
Oloyede S, Ozuomba P, Asuquo L. Olatomiwa and O. Longe, Data-driven techniques for temperature data prediction: big data analytics approach, Environ Monit Assess. 2023;195-343:1-21,
Andrae and T. Edler, On global electricity usage of communication technology: Trends to 2030. Challenges. 2015; 6(1):117-157.
Sheikhzadeh S, Javan M. Key Technologies in 5G: Air Interface. Modares Journal of Electrical Engineering. 2016;16(2):50-61.
Wong V, Schober R, Ng W, Wang L. Key technologies for 5G wireless systems, Cambridge, U.K.: Cambridge University Press; 2017.
Al-Falahy N, Alani O. Technologies for 5G Networks: Challenges and Opportunities. IT Professional. 2017;19(1):12-20.
Stoynov V, Poulkov V, Iliev G, Koleva P. Ultra-Dense Networks: Taxonomy and Key Performance Indicators. Symmetry. 2022; 15:1.
Yu W, Xu H, Zhang H, Griffith D, Golmie N. Ultra-Dense Networks: Survey of State of the Art and Future Directions, in 25th International Conference on Computer Communication and Networks (ICCCN), California; 2016.
Usama M. A survey on recent trends and open issues in energy efficiency of 5G," Sensors. 2018;19(14):3126.
Liu B Natarajan, Xia H. Small cell base station sleep strategies for energy efficiency. IEEE Transactions on Vehicular Technology. 2016;65(3):1652-1661.
Celebi H, Güvenç I. Load analysis and sleep mode optimization for energy-efficient 5G small cell networks, in IEEE International Conference on Communications Workshops (ICC Workshops), Paris; 2017.
Zhang Q, Xu X, Zhang J, Tao X, Liu C. Dynamic Load Adjustments for Small Cells in Heterogeneous Ultra-dense Networks, in IEEE Wireless Communications and Networking Conference (WCNC), Seoul; 2020.
Rehan S, Grace D. Efficient Joint Operation of Advanced Radio Resource and Topology Management in Energy-Aware 5G Networks, in IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston; 2015.
Lin Y, Wang L, Lin P. SES: A novel yet simple energy saving scheme for small cells. IEEE Transactions on Vehicular Technology. 2017;66(9):8347-8356.
Ebrahim, Alsusa E. Interference and resource management through sleep mode selection in heterogeneous networks. IEEE Transactions on Communications. 2017;65 (1):257-269.
Saeed E, Katranaras A, Zoha A, Imran M Imran, Dianati M. Energy efficient resource allocation for 5G Heterogeneous Networks, in IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), Guildford; 2015.
Huo L, Jiang D, Lv Z. Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks, Computers & Electrical Engineering. 2018; 66:316-331.
Malini and K. Babu, Soft frequency reuse based interference minimization technique for long term evolution-advanced heterogeneous networks, in International Conference on Communication and Signal Processing (ICCSP), Chennai; 2017.
Shen Z, Lei X Huang, Chen Q. An Interference Contribution Rate Based Small Cells On/Off Switching Algorithm for 5G Dense Heterogeneous Networks. IEEE Access. 2018;6(1):29757-29769.
Huo L, Jiang D, Lv Z. Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks, Computers & Electrical Engineering. 2018;66:316-331.