Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

Igor Tomic,Predrag Ivanis,Eoin Bleakley

doi:10.3390/electronics11040626

Igor Tomic, Predrag Ivanis + Show 1 more

Open Access

https://doi.org/10.3390/electronics11040626

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Abstract

Network performance prediction is crucial for enabling agile capacity planning in mobile networks. One of the key problems is predicting evolution of spectral efficiency in growing network load conditions. The main factor driving network performance and spectral efficiency is reportedly the Channel Quality Indicator (CQI). In this paper, the performance of different Machine Learning (ML) models were examined, and XGBoost was selected as the best performing model. Furthermore, to improve modeling accuracy, several features were introduced (operating frequency band, Physical Resource Block (PRB) utilization in surrounding cells, number of surrounding cells within a radius, heavy data factor and higher order modulation usage). The impact of these features on CQI prediction were examined.

Highlights

Capacity planning for mobile networks has been a challenge for network planners over the past decade
The main contribution of conducted research is the application of Machine Learning (ML) techniques to problems of network design and capacity planning, and, in particular, spectral efficiency
The main contribution of conducted research is the application of ML techniques to problems of network design and capacity planning, and, in particular, spectral efficiency evolution modeling in growing network load conditions

Summary

Introduction

Capacity planning for mobile networks has been a challenge for network planners over the past decade. The growth rate has varied by market, but on average traffic has doubled every two years [1]. This pace of development mirrors Moore’s Law. In parallel with growing load, network performance dynamically changes, with expected performance downgrade [2] in cases where there is a lack of investments in additional capacity. There is always huge pressure to justify all Capital Expenditure (CapEx) investments In such circumstances, predictive planning is a must. The decision-making process on capacity addition needs to be based on the accurate estimation of future network performance, and what-if evaluations of different scenarios of traffic growth, network performance and capacity expansions

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