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Wavelet Transform Processing for Cellular Traffic Prediction in Machine Learning Networks
Date: 2016/5/21             Browse: 235

Paper author list:

Yunjuan Zang 1, Feixiang Ni 1, Zhiyong Feng 2, Shuguang Cui1,3, and Zhi Ding 1,4

1. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

2. Beijing University of Posts and Telecommunications, Beijing, China

3. Dept. of ECE, Texas A&M University, College Station, TX, USA

4. Dept. of ECE, UC Davis, Davis, CA, USA

 

Published time

2015.07

 

Paper title

Wavelet Transform Processing for Cellular Traffic Prediction in Machine Learning Networks

 

Paper abstract

The ability for cellular operators to closely predict the network traffic volume at various locations can be very important for their resource management and dynamic network control including offloading. This work investigates the analysis of the spatial-temporal information of cellular traffic flow and the prediction of cell-station traffic volumes. Based on the integration of K-means clustering, Elman Neural Network (Elman-NN), and wavelet decomposition methods, we characterize the performance comparison of traffic volume prediction. We tested our wavelet decomposition based machine learning approach using the real traffic data recorded at a district in a big city and demonstrated the gain over traditional approaches.

 

Conference or journals name and website

The Third IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP 2015)

http://www.chinasip2015.org/

 

A short description of the conference or the journal:

The Third IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP 2015) was held in Chengdu, China from 12th to 15th July 2015. Sponsored by the IEEE Signal Processing Society (SPS), ChinaSIP is an annual summit and international conference held in China for domestic and international scientists, researchers, and practitioners to network and discusses the latest progress in theoretical, technological, and educational aspects of signal and information processing.