Prediction of Vertical Handover Using Multivariate Regression Model

Author:

Abstract

Choosing the best performing network among the pool of available networks is a challenge faced by every mobile user. This paper majorly focuses on the development of a system that can ease the above task based on available parameters like Received Signal Strength, Bandwidth, Cost, Network Coverage, Jitter, Packet Loss, Latency etc. The system uses Multivariate Regression Model to help select the best network with the help of a mobile application. The algorithm assigns a Handover value to each available network among the pool of available networks and makes a comparative study to facilitate the decision of the best performing network. The system can also be used by service providers to optimise their network performance by studying the effect of change in the network parameters. Another possible application can be to check the Handover feasibility of future technology networks. The paper also tries to focus on the giving insights on the influence each network parameter mentioned above has on the final value of Handover by using statistical methods and ANOVA.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Electrical and Electronic Engineering,Mechanics of Materials,Civil and Structural Engineering,General Computer Science

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Algorithm for Vertical Handover based on Least Square Weighting techniques;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

2. Designing a Vertical Handover Algorithm for Security-Constrained Applications;Applied Sciences;2023-02-08

3. Algorithm for vertical handover decision using geometric mean and MADM techniques;International Journal of Information Technology;2022-05-16

4. Algorithm for Network Selection based on SAW and MEW techniques;2021 International Conference on Control, Automation, Power and Signal Processing (CAPS);2021-12-10

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