Optimizing Call Drops in Cellular Network using Artificial Intelligence based Handover Schema

Singh, Avinash; Singh, Surya Pratap; Tripathi, Upendra Nath; Mishra, Manish Kumar

doi:10.17148/ijarcce.2017.6155

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Literature Review. Estimating channel for handover applications is broadly classified into two major algorithms [1,7,8]. (1) Real-time monitoring and (2) mathematical modelling or ML-based estimations.…”

Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

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Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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“…

BackgroundMobile subscribers worldwide are grappling with the issue of call-drops (Singh et al, 2017;Tarkaa & Pahalson, 2019;Gaur, 2016). Mobile subscribers cited call-drop as the most common complaint in a recent Pew Internet and American Life Project study (Jan & Lee, 2012).

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mentioning

confidence: 99%

Estimating Call-Drop Probability Rates among Wireless Cellular Networks in Ghana

Agbesi¹,

Louis²

2021

ijird

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The increasing rate of call-drop among wireless cellular networks has become a headache for subscribers of mobile voice networks in Ghana. Despite the many studies that have been carried out on call-drop, most of them have failed to examine call-drop probability rates, a key metric of tackling the issue of call-drop. This study combines the probability distribution function of call-drop and maximum likelihood estimator to estimate call-drop probability rates among the wireless cellular networks in Ghana. The call-drop of the networks was, first of all, fitted to a probability distribution. The call-drop probability rate was then estimated using the maximum likelihood estimator of the fitted distribution. The call-drop probability distribution of Airtel-Tigo, Vodafone, and Glo follow the Geometric probability distribution, while that of MNT follows the Negative Binomial probability distribution. The call-drop probability rate of MTN is 7.6%, Vodafone is 3.3%, Airtel-Tigo, and Glo is 3.0%. These call-drop rates are found to be outside the prescribed NCA's acceptable limit of less than 3 %.

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Big data and predictive analytics and Malaysian micro-, small and medium businesses

Vachkova¹,

Ghouri²,

Ashour³

et al. 2023

SN Bus Econ

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Innovative technologies such as big data and predictive analytics are facilitating a global revolution in operational management. The present study aims to investigate the extent of their adoption among micro-, small and medium enterprises. The context of choice is Malaysia—a growing economy hosting some of the relatively more technologically advanced enterprises in ASEAN union. The research plan of the present paper pursued three objectives. First, we aimed to identify the general rate of adoption of BDPA. Second, we explored the most common uses of this set of innovations in Malaysian micro-, small, and medium enterprises. Third, we strove to abstract the antecedents and consequences of BDPA in micro-, small, and large firms in the manufacturing and service sectors. We discovered that BDPA are almost never used to outsource suppliers or to optimise spending and purchasing. Conversely, the most frequent applications of BDPA are in customer relationship management and in warehouse and operations improvement. Moreover, we smaller firms are more resilient to the challenges of adopting BDPA than their smaller and larger counterparts. Thus, this paper presents a snapshot of the BDPA dissemination in the micro-, small and medium ecosystem in Malaysia in 2021, while exploring opportunities for the future development and dissemination of this set of innovations.

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Optimizing Call Drops in Cellular Network using Artificial Intelligence based Handover Schema

Cited by 5 publications

References 6 publications

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Estimating Call-Drop Probability Rates among Wireless Cellular Networks in Ghana

Big data and predictive analytics and Malaysian micro-, small and medium businesses

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