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 TransformProcessing 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 journal’s name and website：
The Third IEEE China Summitand International Conference on Signal and Information Processing (ChinaSIP2015)
A short description of theconference or the journal:
The Third IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP2015) was held in Chengdu, China from 12th to 15th July 2015. Sponsored by the IEEE Signal Processing Society (SPS), China SIP 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.
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