ABSTRACT
In smart cities, green cellular networks play a crucial role to support wireless access for numerous devices anywhere and anytime with efficiency and sustainability. Because base stations (BSes) consume more than 70% of overall cellular network infrastructure energy, saving the power consumption of BSes is the key task to build a green cellular network. Except for low power design of the BS hardware and software, the traffic-driven BS sleeping operation is an economical way to improve existing cellular networks, which can reduce the BS power consumption at low traffic load. However, prior BS sleeping strategies establish on the static temporal characteristics of traffic load, which ignore the fact that network traffic is influenced by many factors such as time, human mobility, holiday, weather, etc. Hence, prior traffic estimation is coarse, and the BS sleeping strategies cannot apply to the changing network traffic. In this paper, we exploit a machine learning method to estimate the BS traffic and propose a BS sleeping strategy based on predicted traffic for power saving in the cellular network. We analyze network traffic in multi-views: temporal influence, spatial influence, and event influence. Then, we propose a multi-view ensemble learning model to predict network traffic load, which learns the traffic in multi-views and combine the results with ensemble. Furthermore, we formulate a BS sleeping strategy based on the predicted traffic load. Finally, we evaluate our traffic prediction algorithm on real cellular network data. The evaluation shows that our traffic prediction algorithm improves about 40% than state-of-the-art machine learning methods. Also, we evaluate the proposed BS sleeping strategy, which yields about 10% more energy savings and less device damage than the competitors in the simulated environment.
- Oliver Arnold, Fred Richter, Gerhard Fettweis, and Oliver Blume. 2010. Power consumption modeling of different base station types in heterogeneous cellular networks. In Future Network and Mobile Summit, 2010.Google Scholar
- Imran Ashraf, Federico Boccardi, and Lester Ho. 2011. Sleep mode techniques for small cell deployments. IEEE Communications Magazine (2011).Google Scholar
- Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. 1984. Classification and regression trees. CRC press.Google Scholar
- Peter J Brockwell and Richard A Davis. 2016. Introduction to time series and forecasting. springer.Google Scholar
- Bin Cao, Jing Fan, Mingxuan Yuan, and Yanhua Li. 2016. Toward accurate energy-efficient cellular network: switching off excessive carriers based on traffic profiling. In Proceedings of the 31st Annual ACM Symposium on Applied Computing. Google ScholarDigital Library
- Shuguang Cui, Andrea J Goldsmith, and Ahmad Bahai. 2004. Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE Journal on selected areas in communications (2004). Google ScholarDigital Library
- Aleksandar Damnjanovic, Juan Montojo, Yongbin Wei, Tingfang Ji, Tao Luo, Madhavan Vajapeyam, Taesang Yoo, Osok Song, and Durga Malladi. 2011. A survey on 3GPP heterogeneous networks. IEEE Wireless Communications (2011).Google Scholar
- Weiwei Duan and Yao-Yi Chiang. 2016. Building knowledge graph from public data for predictive analysis: a case study on predicting technology future in space and time. In SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. Google ScholarDigital Library
- Gerhard Fettweis and Ernesto Zimmermann. 2008. ICT energy consumption-trends and challenges. In Proceedings of the 11th international symposium on wireless personal multimedia communications.Google Scholar
- Fabien Girardin, Andrea Vaccari, Alexandre Gerber, Assaf Biderman, and Carlo Ratti. 2009. Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. In Intl. Conference on Computers in Urban Planning and Urban Management.Google Scholar
- Congzheng Han, Tim Harrold, Simon Armour, Ioannis Krikidis, Stefan Videv, Peter M Grant, Harald Haas, John S Thompson, Ivan Ku, Cheng-Xiang Wang, and others. 2011. Green radio: radio techniques to enable energy-efficient wireless networks. IEEE communications magazine (2011).Google Scholar
- Minh X Hoang, Yu Zheng, and Ambuj K Singh. 2016. FCCF: forecasting citywide crowd flows based on big data. In SIGSPATIAL. Google ScholarDigital Library
- Nahina Islam, Sithamparanathan Kandeepan, and James Scott. 2015. Energy efficiency of cellular base stations with ternary-state transceivers. In ICSPCS.Google Scholar
- Jinseong Jeong, Donald F Kimball, Myoungbo Kwak, Paul Draxler, Chin Hsia, Craig Steinbeiser, Thomas Landon, Oleh Krutko, Lawrence E Larson, and Peter M Asbeck. 2009. High-efficiency WCDMA envelope tracking base-station amplifier implemented with GaAs HVHBTs. IEEE journal of solid-state circuits (2009).Google Scholar
- Ram Keralapura, Antonio Nucci, Zhi-Li Zhang, and Lixin Gao. 2010. Profiling users in a 3g network using hourglass co-clustering. In Proceedings of the sixteenth annual international conference on Mobile computing and networking. Google ScholarDigital Library
- Chang Li, Jun Zhang, and Khaled Ben Letaief. 2013. Energy efficiency analysis of small cell networks. In ICC.Google Scholar
- Ming Li, Pan Li, Xiaoxia Huang, Yuguang Fang, and Savo Glisic. 2015. Energy consumption optimization for multihop cognitive cellular networks. IEEE Transactions on Mobile Computing (2015).Google Scholar
- Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jacques Palicot, and Honggang Zhang. 2014. TACT: A transfer actor-critic learning framework for energy saving in cellular radio access networks. IEEE transactions on wireless communications (2014).Google Scholar
- Rongpeng Li, Zhifeng Zhao, Xuan Zhou, and Honggang Zhang. 2014. Energy savings scheme in radio access networks via compressive sensing-based traffic load prediction. Transactions on Emerging Telecommunications Technologies (2014). Google ScholarDigital Library
- Yexin Li, Yu Zheng, Huichu Zhang, and Lei Chen. 2015. Traffic prediction in a bike-sharing system. In SIGSPATIAL. Google ScholarDigital Library
- Andy Liaw and Matthew Wiener. 2002. Classification and regression by random-Forest. R news 2, 3 (2002), 18--22.Google Scholar
- Christopher D Manning, Prabhakar Raghavan, Hinrich Schütze, and others. 2008. Introduction to information retrieval. Google ScholarDigital Library
- Marco Ajmone Marsan, Luca Chiaraviglio, Delia Ciullo, and Michela Meo. 2013. On the efectiveness of single and multiple base station sleep modes in cellular networks. Computer Networks (2013). Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. Google ScholarDigital Library
- Diala Naboulsi, Marco Fiore, Stephane Ribot, and Razvan Stanica. 2016. Large-scale mobile traffic analysis: a survey. IEEE Communications Surveys & Tutorials (2016).Google Scholar
- Diala Naboulsi, Razvan Stanica, and Marco Fiore. 2014. Classifying call profiles in large-scale mobile traffic datasets. In INFOCOM.Google Scholar
- Eduardo Mucelli Rezende Oliveira, Aline Carneiro Viana, Kolar Purushothama Naveen, and Carlos Sarraute. 2015. Measurement-driven mobile data traffic modeling in a large metropolitan area. In PerCom.Google Scholar
- Utpal Paul, Anand Prabhu Subramanian, Milind Madhav Buddhikot, and Samir R Das. 2011. Understanding traffic dynamics in cellular data networks. In INFOCOM, 2011 Proceedings IEEE.Google ScholarCross Ref
- Chunyi Peng, Suk-Bok Lee, Songwu Lu, Haiyun Luo, and Hewu Li. 2011. Traffic-driven power saving in operational 3G cellular networks. In Proceedings of the 17th annual international conference on Mobile computing and networking. Google ScholarDigital Library
- Louai Saker, Salah-Eddine Elayoubi, Richard Combes, and Tijani Chahed. 2012. Optimal control of wake up mechanisms of femtocells in heterogeneous networks. IEEE Journal on Selected Areas in Communications (2012).Google Scholar
- Mohammad Salahuddin, Khorshed Alam, and Ilhan Ozturk. 2016. The effects of Internet usage and economic growth on CO2 emissions in OECD countries: A panel investigation. Renewable and Sustainable Energy Reviews (2016).Google Scholar
- M Zubair Shafiq, Lusheng Ji, Alex X Liu, and Jia Wang. 2011. Characterizing and modeling internet traffic dynamics of cellular devices. In SIGMETRICS. Google ScholarDigital Library
- Ward Van Heddeghem, Sofie Lambert, Bart Lannoo, Didier Colle, Mario Pickavet, and Piet Demeester. 2014. Trends in worldwide ICT electricity consumption from 2007 to 2012. Computer Communications (2014). Google ScholarDigital Library
- Guoxiang Wang, Caili Guo, Shengsen Wang, and Chunyan Feng. 2013. A traffic prediction based sleeping mechanism with low complexity in femtocell networks. In ICC.Google Scholar
- Yi Wang, Michalis Faloutsos, and Hui Zang. 2013. On the usage patterns of multi-modal communication: Countries and evolution. In Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference on.Google ScholarCross Ref
- Carey Williamson, Emir Halepovic, Hongxia Sun, and Yujing Wu. 2005. Characterization of CDMA2000 cellular data network traffic. In Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on. Google ScholarDigital Library
- Fei Wu, Hongjian Wang, and Zhenhui Li. 2016. Interpreting traffic dynamics using ubiquitous urban data. In SIGSPATIAL. Google ScholarDigital Library
- Jingjin Wu, Yujing Zhang, Moshe Zukerman, and Edward Kai-Ning Yung. 2015. Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey. IEEE communications surveys & tutorials (2015).Google Scholar
- Liu Ximei. 2016. http://www.escn.com.cn/news/show-334775.html. (2016).Google Scholar
- Cong Xiong, Geoffrey Ye Li, Shunqing Zhang, Yan Chen, and Shugong Xu. 2012. Energy-efficient resource allocation in OFDMA networks. IEEE Transactions on Communications (2012).Google Scholar
- Zheng Yang, Jonathan Roth, and Rishee K Jain. 2016. Data-driven benchmarking of building energy performance at the city scale. In SIGSPATIAL Workshop on Smart Cities and Urban Analytics. Google ScholarDigital Library
- Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li. 2016. ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data. In IJCAI. Google ScholarDigital Library
- Ming Zeng, Tong Yu, Xiao Wang, Vincent Su, Le T Nguyen, and Ole J Mengshoel. 2016. Improving Demand Prediction in Bike Sharing System by Learning Global Features. Machine Learning for Large Scale Transportation Systems (LSTS)@ KDD-16 (2016).Google Scholar
- Ying Zhang and Ake Årvidsson. 2012. Understanding the characteristics of cellular data traffic. ACM SIGCOMM Computer Communication Review (2012). Google ScholarDigital Library
- Shenglin Zhao, Irwin King, Michael R Lyu, Jia Zeng, and Mingxuan Yuan. 2017. Mining Business Opportunities from Location-based Social Networks. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1037--1040. Google ScholarDigital Library
Index Terms
- Traffic Prediction Based Power Saving in Cellular Networks: A Machine Learning Method
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