DocumentCode :
3496030
Title :
Spatial-temporal compressed sensing based traffic prediction in cellular networks
Author :
Wen, Qian ; Zhao, Zhifeng ; Li, Rongpeng ; Zhang, Honggang
Author_Institution :
York-Zhejiang Lab. for Cognitive Radio & Green Commun., Zhejiang Univ., Hangzhou, China
fYear :
2012
fDate :
15-17 Aug. 2012
Firstpage :
119
Lastpage :
124
Abstract :
In conventional cellular networks, base stations (B-Ss) usually suffer from severe power consumption since they are working to guarantee the coverage and QoS (quality-of-service) requirement according to the peak traffic load generated by the mobile cellular users Accordingly, how to precisely forecast the future traffic load to promote network cooperation and adaptive energy resource allocation in complying with the variation of spatial-temporal traffic load has been an emerged issue due to the significant energy exhaustion of BSs. In this paper, we propose a spatial-temporal compressed sensing based network traffic prediction method to solve this problem. We first construct a traffic matrix (TM) by using previously measured data and setting the data to be predicted as zeros, corresponding to the volume of traffic load. Then, compressed sensing approach with large scale and small scale temporal constraints as well as spatial constraints is employed to factorize the traffic matrix. By reuniting the results of traffic matrix factorization, we obtain the estimation of predicted traffic data. Numerical results have showed that this method can restrict the prediction error under 10% when dealing with real traffic load data.
Keywords :
cellular radio; matrix decomposition; quality of service; signal reconstruction; telecommunication traffic; BS; QoS; TM; adaptive energy resource allocation; base stations; cellular networks; mobile cellular user; network cooperation; power consumption; quality-of-service; spatial-temporal compressed sensing; traffic matrix factorization; traffic prediction; Base stations; Compressed sensing; Load modeling; Matrix decomposition; Mobile communication; Predictive models; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications in China Workshops (ICCC), 2012 1st IEEE International Conference on
Conference_Location :
Bejing
Print_ISBN :
978-1-4673-2996-5
Electronic_ISBN :
978-1-4673-2995-8
Type :
conf
DOI :
10.1109/ICCCW.2012.6316465
Filename :
6316465
Link To Document :
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