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Handoff in 5g Ultra Dense Networks Using Fixed Sphere Precoding


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1 College of Computer Science and Information Science, Srinivas University, India
     

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It is anticipated that the millimetrewave, often known as mm-wave, technology that will be used in 5G networks will greatly enhance network capacity. The mm-wave signals, on the other hand, are prone to obstructions than the ones at lower bands; this demonstrates the impact that route loss has on the network coverage. Because of the fractal nature of cellular coverage and the different path loss exponents that apply to different directions, it has been suggested that a route loss model in a multi-directional manner for 5G UDN networks. This is due to the fact that different directions have path loss exponents. In addition, the proposed loss model is applied to the 5G ultra-dense network in order to calculate the coverage probability, association probability, and handoff probability (UDN). According to the numerical findings of this research, in 5G UDN, the influence of anisotropic path loss increases the association probability with long link distance. It has also come to light that the performance of the handoff suffers tremendously as a consequence of the anisotropic propagation environment. A new difficulty has arisen for 5G UDN as a consequence of the substantial handoff overhead that has been produced.

Keywords

Fractal Characteristics, Multi-Directional Path Loss, Cellular Coverage Ultra-Dense Network
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  • I. Rabet, S.P. Selvaraju and M. Bjorkman, “Poster: Particle Filter for Handoff Prediction in SDN-based IoT Networks”, Proceedings of International Conference on Wireless Networks, pp. 172-173, 2020.

  • A.C. Marino and J.A. Mazer, “Saccades Trigger Predictive Updating of Attentional Topography in Area V4”, Neuron, Vol. 98, No. 2, pp. 429-438, 2018.

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  • Handoff in 5g Ultra Dense Networks Using Fixed Sphere Precoding

Abstract Views: 164  |  PDF Views: 1

Authors

V. Saravanan
College of Computer Science and Information Science, Srinivas University, India
A. Jayanthiladevi
College of Computer Science and Information Science, Srinivas University, India

Abstract


It is anticipated that the millimetrewave, often known as mm-wave, technology that will be used in 5G networks will greatly enhance network capacity. The mm-wave signals, on the other hand, are prone to obstructions than the ones at lower bands; this demonstrates the impact that route loss has on the network coverage. Because of the fractal nature of cellular coverage and the different path loss exponents that apply to different directions, it has been suggested that a route loss model in a multi-directional manner for 5G UDN networks. This is due to the fact that different directions have path loss exponents. In addition, the proposed loss model is applied to the 5G ultra-dense network in order to calculate the coverage probability, association probability, and handoff probability (UDN). According to the numerical findings of this research, in 5G UDN, the influence of anisotropic path loss increases the association probability with long link distance. It has also come to light that the performance of the handoff suffers tremendously as a consequence of the anisotropic propagation environment. A new difficulty has arisen for 5G UDN as a consequence of the substantial handoff overhead that has been produced.

Keywords


Fractal Characteristics, Multi-Directional Path Loss, Cellular Coverage Ultra-Dense Network

References