Traffic Prediction in Telecom Systems Using Deep Learning

The deep neural network implementation in this work analyses, evaluates and generates predictions based on the open source big data of telecommunications activity released by Telecom Italia. The deep learning library used for the neural network implementation is Tensorflow which contains many high and mid-level APIs to achieve the functionality. The model uses random data from the test dataset for generating predictions and Estimator API of Tensorflow for building the neural network. Also Adam optimizer is used for optimizing the loss function with the model’s resulting efficiency to be around 98.6–99.8%.

[1]  Lei Guo,et al.  Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).

[2]  K. P. Soman,et al.  Applying deep learning approaches for network traffic prediction , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[3]  Arief B. Koesdwiady,et al.  Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[4]  Zhifeng Zhao,et al.  The predictability of cellular networks traffic , 2012, 2012 International Symposium on Communications and Information Technologies (ISCIT).

[5]  Marco Gramaglia,et al.  Mobile traffic forecasting for maximizing 5G network slicing resource utilization , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[6]  Chih-Wei Huang,et al.  A study of deep learning networks on mobile traffic forecasting , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[7]  Ramez Elmasri,et al.  Scalable deep traffic flow neural networks for urban traffic congestion prediction , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[8]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[9]  Sanghoon Bae,et al.  Deep Neural Networks for traffic flow prediction , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[10]  Geoffrey Ye Li,et al.  Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems , 2017, IEEE Wireless Communications Letters.

[11]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[12]  Marco De Nadai,et al.  A multi-source dataset of urban life in the city of Milan and the Province of Trentino , 2015, Scientific Data.

[13]  Zhifeng Zhao,et al.  The Learning and Prediction of Application-Level Traffic Data in Cellular Networks , 2016, IEEE Transactions on Wireless Communications.

[14]  Adam Kowalczyk,et al.  Experiments with Simple Neural Networks for Real-Time Control , 1997, IEEE J. Sel. Areas Commun..