Abstract:Concerning the problem that AutoRegressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) unit do not utilize the collaboration between Base Stations (BSs) in traffic prediction, a new method called Traffic Prediction based on Space Collaboration (TPBC) which uses the collaboration between BSs produced by users was proposed. Firstly, a BS cooperative network was constructed based on the collaboration between BSs and then divided into multiple communities. Next, the cooperative BSs, which have the closest relationships with the target BS in the same community, were found via Granger causality test. Finally, a hybrid neural network was constructed by LSTM and Embedding layer, and the historial traffic of target BS and each cooperative BS was utilized for traffic prediction of target BS. The experimental results show that the Root Mean Square Error (RMSE) of TPBC is reduced by 29.19% and 27.47% compared with ARIMA and LSTM respectively. It shows that TPBC has the capability of improving the accuracy of BS traffic prediction effectively, which benefits traffic offloading and energy saving.
彭铎, 周建国, 羿舒文, 江昊. 基于空间合作关系的基站流量预测模型[J]. 计算机应用, 2019, 39(1): 154-159.
PENG Duo, ZHOU Jianguo, YI Shuwen, JIANG Hao. Base station traffic prediction model based on spatial collaboration. Journal of Computer Applications, 2019, 39(1): 154-159.
[1] YIGITEL M A, INCEL O D, ERSOY C. QoS vs. energy:a traffic-aware topology management scheme for green heterogeneous networks[J]. Computer Networks, 2015, 78:130-139. [2] SAXENA N, SAHU B J R, HAN Y S. Traffic-aware energy optimization in green LTE cellular systems[J]. IEEE Communications Letters, 2014, 18(1):38-41. [3] HUANG C M, CHIANG M S, DAO D T, et al. V2V data offloading for cellular network based on the Software Defined Network (SDN) inside Mobile Edge Computing (MEC) architecture[J]. IEEE Access, 2018, 6(99):17741-17755. [4] CHEN Q, YU G, SHAN H, et al. Cellular meets WiFi:traffic offloading or resource sharing?[J]. IEEE Transactions on Wireless Communications, 2016, 15(5):3354-3367. [5] LI Q, JIANG H, LI Y, et al. Collective human mobility patterns:a case study using data usage detail records[C]//Proceedings of the 2018 IEEE International Conference on Internet of Things. Piscataway, NJ:IEEE, 2018:17-22. [6] BECKER R, HANSON K, ISAACMAN S, et al. Human mobility characterization from cellular network data[J]. Communications of the ACM, 2013, 56(1):74-82. [7] SHU Y, YU M, LIU J, et al. Wireless traffic modeling and prediction using seasonal ARIMA models[C]//Proceedings of the 2003 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2003:1675-1679. [8] ZHOU B, HE D, SUN Z. Traffic modeling and prediction using ARIMA/GARCH model[M]//Modeling and Simulation Tools for Emerging Telecommunication Networks. Berlin:Springer, 2006:101-121. [9] 胡昕.基于RNN的网络安全态势预测方法[J].现代计算机,2017(6):14-16.(HU X. Prediction of network security situation based on RNN[J]. Modern Computer, 2017(6):14-16.) [10] 郑毅,李凤,张丽,等.基于长短时记忆网络的人体姿态检测方法[J].计算机应用,2018,38(6):1568-1574.(ZHENG Y, LI F, ZHANG L, et al. Human posture detection method based on long short term memory network[J]. Journal of Computer Applications, 2018, 38(6):1568-1574.) [11] 滕飞,郑超美,李文.基于长短期记忆多维主题情感倾向性分析模型[J].计算机应用,2016,36(8):2252-2256.(TENG F, ZHENG C M, LI W. Multidimensional topic model for oriented sentiment analysis based on long short-term memory[J]. Journal of Computer Applications, 2016, 36(8):2252-2256.) [12] ZHOU X, ZHAO Z, LI R, et al. The predictability of cellular networks traffic[C]//Proceedings of the 2012 International Symposium on Communications and Information Technologies. Piscataway, NJ:IEEE, 2012:973-978. [13] HUANG C W, CHIANG C T, LI Q. A study of deep learning networks on mobile traffic forecasting[C]//Proceedings of the 2017 IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications. Piscataway, NJ:IEEE, 2017:1-6. [14] WANG J, TANG J, XU Z, et al. Spatiotemporal modeling and prediction in cellular networks:a big data enabled deep learning approach[C]//IEEE INFOCOM 2017:Proceedings of the 2017 IEEE Conference on Computer Communications. Piscataway, NJ:IEEE, 2017:1-9. [15] JIANG H, YI S, WU L, et al. Data-driven cell zooming for large-scale mobile networks[J]. IEEE Transactions on Network & Service Management, 2018, 15(1):156-168. [16] PAUL U, SUBRAMANIAN A P, BUDDHIKOT M M, et al. Understanding traffic dynamics in cellular data networks[C]//INFOCOM 2011:Proceedings of the 201130th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies. Piscataway, NJ:IEEE, 2011:882-890. [17] DELVENNE J C, YALIRAKI S N, BARAHONA M. Stability of graph communities across time scales[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(29):12755-12760. [18] DELVENNE J C, SCHAUB M T, YALIRAKI S N, et al. The stability of a graph partition:a dynamics-based framework for community detection[M]//Dynamics on and of Complex Networks. Berlin:Springer, 2013:221-242. [19] SCHAUB M T, DELVENNE J C, YALIRAKI S N, et al. Markov dynamics as a zooming lens for multiscale community detection:non clique-like communities and the field-of-view limit[J]. PLoS One, 2012, 7(2):e32210. [20] SETH A K. A MATLAB toolbox for Granger causal connectivity analysis.[J]. Journal of Neuroscience Methods, 2010, 186(2):262-273. [21] BENGIO Y, VINCENT P, JANVIN C. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3(6):1137-1155. [22] SOCHER R, BAUER J, MANNING C D, et al. Parsing with compositional vector grammars[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2013:455-465.