Root cause detection of Call drops in live GSM network

Sudhindra, K. R.; Sridhar, Varadharajan

doi:10.1109/tencon.2011.6129142

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…The use of ANN for the development of the model in this work has the advantage of training any network and prediction as long as correct data were supplied. The paper described a good approach for call drops detection as in [11]- [15] by using different scenarios. It has been demonstrated that a classification of call drop events can be made with this approach based on five GSM parameters, namely RxLev, RxQual, FER, BER and TA.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Call Drops in GSM Network using Artificial Neural Network

Erunkulu

Onwuka

Ugweje

et al. 2019

Jurnal Teknologi dan Sistem Komputer

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Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network.

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Section: Resultsmentioning

confidence: 99%

“…These predictions were based on acquired data from servers that had recorded dropped call events over periods of time. Another researcher collected the TCH (traffic channel) call drop events from a live GSM network and used ANN to predict the probability of drop call [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Call Drops in GSM Network using Artificial Neural Network

Erunkulu

Onwuka

Ugweje

et al. 2019

Jurnal Teknologi dan Sistem Komputer

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“…In literature, different attempts have been adopted to examine and model the rate of call drops in cellular mobile networks, but mostly through theoretical and analytical methods [5]- [14], which are too complex and impracticable to explore during network planning/re-planning phase [15][16][17][18][19]. In [20][21][22][23][24][25][26][27][28][29], the authors engaged combined theoretical survey and analytical methods to study wireless network performance using some key performance indicators such as signal quality [20,21], blocking/dropping probabilities in [22][23][24][25], grade of service in [26], traffic delay in [27], outage probability in [28], spectral efficiency in [29], and cell availability in [30], respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator

Ojuh¹,

Isabona²

2021

IJMSC

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Mobile phones and handsets enable us to communicate our voice, data and video messages with individuals that are far-off from us. When an active call is initiated by someone using a mobile phone, it is transmitted through a nearby Base Station (BS) transmitter to another BS until the call gets to its intended receiver. Any time a caller initiates and loses a connection to a BS while on conversation, the call is said to be dropped. The initiation and completion of an active call without any form of disconnection or termination is a key service quality parameter in telecommunication system networks. Robust statistical estimation, modelling and characterization of call drop rates is of high importance to the network operators and radio frequency engineers for effective re-planning and performance management process of telecommunication system networks. This work was designed to determine the optimal probability distribution model for drop call rates based on a five week acquired rate of drop calls data sample in the Southern regions of Nigeria. To accomplish the aim, eight probability distributions namely logistic, log-logistic, normal, log-normal, exponential, Rayleigh, rician and Gumbel max were explored and based on the combined scores of three goodness of fit statistical tests, the log-logistic distribution was found to be the optimal probability distribution for the weekly rate of drop call prognostic analysis. The results could be of immense assistance to radio frequency engineers for optimal statistical modelling and design of cellular systems channels.

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“…When there is no direct line of sight between the sending and receiving antennas, the Rayleigh effect occurs because of the rapid change in the received signal level, both in terms of amplitude and phase. Chandler (2020) Existence of weak coverage area and weak radio signal, intra-network interference, unreasonable radio parameter settings, equipment hardware faults such as low output power of the power amplifier, large differences among different carrier transmission, power, carrier transmitter fault, combiner fault, and splitter fault, faults in antenna feeder system, and weak battery power, are all reported as causes of radio link failure call drop existence of interference such as intranetwork interference due to unreasonable frequency planning and other external interference, equipment hardware fault, such as clock fault in destination cell or in source cell, low output power of the power amplifier, large difference among different transmitter's transmission power, transmitter fault, combiner fault, and divider fault, and unreasonable radio parameter settings, are the causes of handover failure call drop according to Sudhindra and Sridhar (2011).…”

Section: Causes Of Call-drop In Mobile Cellular Networkmentioning

confidence: 99%

An Investigation of Factors Influencing Call-Drop among Wireless Cellular Network Users in Ghana

Agbesi¹,

Louis²

2022

theijst

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The aim of the study was to examine the factors influencing call-drops among wireless cellular network users in Ghana. A total of 1109 wireless cellular network users participated in the study. Exploratory and confirmatory factor analyses were used to find out the latent factors influencing the call-drops among wireless cellular network users in Ghana. The results of the exploratory and confirmatory factor analyses revealed eight-factor constructs. Among these factors, limited cell towers, cell tower overload, and general power problems were found to be the highest contributory factors. The combined three factors accounted for almost 57% of the total variance. The study's results reveal that network factors play a major role among the factors influencing call-drops in Ghana. This implies that in dealing with call-drop problems in Ghana, policymakers, regulators, and stakeholders should pay attention to the network factors of wireless cellular networks.

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Root cause detection of Call drops in live GSM network

Cited by 5 publications

References 5 publications

Prediction of Call Drops in GSM Network using Artificial Neural Network

Prediction of Call Drops in GSM Network using Artificial Neural Network

Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator

An Investigation of Factors Influencing Call-Drop among Wireless Cellular Network Users in Ghana

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