Machine Learning Model for GSM BSC Control Plane Units
Aswathy K1, Kaustuv Saha2, Pardhasaradhi R3, Athi Narayanan4
1Aswathy K, Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham Amritapuri, India.
2Kaustuv Saha, Nokia Solutions and Networks, Pvt. Ltd. Bengaluru (Karnataka), India.
3Pardhasaradhi R, Nokia Solutions and Networks, Pvt. Ltd. Bengaluru (Karnataka), India.
4Athi Narayanan, Nokia Solutions and Networks, Pvt. Ltd. Bengaluru (Karnataka), India.
Manuscript received on 15 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 06 September 2019 | PP: 219-223 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10440886S19/19©BEIESP | DOI: 10.35940/ijeat.F1044.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model.
Keywords: Machine Learning, Base Station Controller, Neural Networks, Support Vector Regression, Regression Model.
Scope of the Article: Machine Learning