Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach
By Jing Wang, Jian Tang, Zhiyuan Xu, Yanzhi Wang, Guoliang Xue, Xing Zhang, Dejun Yang. IEEE Conference on Computer Communications, 2017
353 citations recorded by the source corpus.
Subfields: Building and Construction, Transportation
View Paper: https://www.semanticscholar.org/paper/0bf0da92f0dfe4771a3fbf3fddb376d10c1fd586
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Citations in the corpus
- Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, methodology): “1d shows the spatial correlation in terms of Pearson correlation coefficient ρ, which is a widely adopted metric for measuring spatial correlations [31], [32] and its”
- Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “In the simulations in [62], a 11 × 11 square is used as a neighboring region, i.”
- Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “DNNs also play a critical role in supporting a variety of compelling upper layer applications, such as traffic prediction [327], packet routing [328] and control [329],”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[191] Mobile traffic forecasting AE + LSTM SGD Uses an AE to model spatial correlations and an LSTM to model temporal correlation Zhang and Patras [192] Long-term mobile traffic forecasting ConvLSTM + 3D-CNN Adam Combines 3D-CNNs and ConvLSTMs to perform long-term forecasting”