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Article

A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment

1
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
2
School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada
3
Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada
4
Ericsson Canada, Ottawa, ON K2K 2V6, Canada
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2020, 9(4), 53; https://doi.org/10.3390/jsan9040053
Received: 2 October 2020 / Revised: 3 November 2020 / Accepted: 14 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue 5G and Beyond towards Enhancing Our Future)
Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques. View Full-Text
Keywords: 5G networks; wireless communications; network load predictions; clustering 5G networks; wireless communications; network load predictions; clustering
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MDPI and ACS Style

Mahdy, B.; Abbas, H.; Hassanein, H.S.; Noureldin, A.; Abou-zeid, H. A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment. J. Sens. Actuator Netw. 2020, 9, 53. https://doi.org/10.3390/jsan9040053

AMA Style

Mahdy B, Abbas H, Hassanein HS, Noureldin A, Abou-zeid H. A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment. Journal of Sensor and Actuator Networks. 2020; 9(4):53. https://doi.org/10.3390/jsan9040053

Chicago/Turabian Style

Mahdy, Basma, Hazem Abbas, Hossam S. Hassanein, Aboelmagd Noureldin, and Hatem Abou-zeid. 2020. "A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment" Journal of Sensor and Actuator Networks 9, no. 4: 53. https://doi.org/10.3390/jsan9040053

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