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LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edge (2403.18810v1)

Published 8 Feb 2024 in cs.NI and cs.LG

Abstract: The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support. To manage and maintain large-scale networks, mobile network operators require timely information, or even accurate performance forecasts. In this paper, we propose LightningNet, a lightweight and distributed graph-based framework for forecasting cellular network performance, which can capture spatio-temporal dependencies that arise in the network traffic. LightningNet achieves a steady performance increase over state-of-the-art forecasting techniques, while maintaining a similar resource usage profile. Our architecture ideology also excels in the respect that it is specifically designed to support IoT and edge devices, giving us an even greater step ahead of the current state-of-the-art, as indicated by our performance experiments with NVIDIA Jetson.

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References (63)
  1. N. Agarwal. 2008. Wcdma: Kpi analysis & optimization. Nokia Technologies Co., Ltd..
  2. Md. Ariful Alam. 2013. Mobile Network Planning and KPI Improvement (Dissertation).
  3. James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2016/file/390e982518a50e280d8e2b535462ec1f-Paper.pdf
  4. Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018). arXiv:1806.01261 http://arxiv.org/abs/1806.01261
  5. Y. Benjamini and Y. Hochberg. 1995. Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 1 (1995), 289–300.
  6. Y. Benjamini and Y. Hochberg. 2000. On the adaptive control of the false discovery fate in multiple testing with independent statistics. Journal of Educational and Behavioral Statistics 25, 1 (2000), 60–83.
  7. BRITS: Bidirectional Recurrent Imputation for Time Series. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 6776–6786.
  8. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. CoRR abs/1406.1078 (2014). arXiv:1406.1078 http://arxiv.org/abs/1406.1078
  9. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs/1412.3555 (2014). arXiv:1412.3555 http://arxiv.org/abs/1412.3555
  10. A Recurrent Latent Variable Model for Sequential Data. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS’15). MIT Press, Cambridge, MA, USA, 2980–2988.
  11. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. https://doi.org/10.48550/ARXIV.1802.07007
  12. Traffic Forecasting in Cellular Networks Using the LSTM RNN. In Proceedings of the 22nd Pan-Hellenic Conference on Informatics (Athens, Greece) (PCI ’18). Association for Computing Machinery, New York, NY, USA, 28–33. https://doi.org/10.1145/3291533.3291540
  13. Travel time prediction with LSTM neural network. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). 1053–1058. https://doi.org/10.1109/ITSC.2016.7795686
  14. Ericsson. 2014. Measuring and improving network performance. White paper.
  15. Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). 324–328. https://doi.org/10.1109/YAC.2016.7804912
  16. Short-Term Prediction of Traffic Volume in Urban Arterials. Journal of Transportation Engineering-asce 121 (1995), 249–254.
  17. Relational inductive bias for physical construction in humans and machines. CoRR abs/1806.01203 (2018). arXiv:1806.01203 http://arxiv.org/abs/1806.01203
  18. Haibo He and Edwardo A. Garcia. 2009. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 21, 9 (2009), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
  19. Deep Convolutional Networks on Graph-Structured Data. CoRR abs/1506.05163 (2015). arXiv:1506.05163 http://arxiv.org/abs/1506.05163
  20. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (nov 1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  21. Harri Holma and Antti Toskala. 2007. WCDMA for UMTS: HSPA Evolution and LTE. John Wiley & Sons, Inc., USA.
  22. J J Hopfield. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79, 8 (1982), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.79.8.2554
  23. Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-Term Crowd Flows Prediction. In Proceedings of the 2nd International Conference on Compute and Data Analysis (DeKalb, IL, USA) (ICCDA 2018). Association for Computing Machinery, New York, NY, USA, 28–35. https://doi.org/10.1145/3193077.3193082
  24. Michael I. Jordan. 1997. Serial Order: A Parallel Distributed Processing Approach. Advances in psychology 121 (1997), 471–495.
  25. X. Kaiping and G. Hao. 2008. GSM KPI monitoring and improvement guide. Huawei Technologies Co., Ltd..
  26. Georgios Kalafatas and Srinivas Peeta. 2007. An Exact Graph Structure for Dynamic Traffic Assignment: Formulation, Properties, and Computational Experience. In Transportation Research Board 86th Annual Meeting.
  27. Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. CoRR abs/1609.02907 (2016). arXiv:1609.02907 http://arxiv.org/abs/1609.02907
  28. Algorithms for Graph Similarity and Subgraph Matching.
  29. Ralf Kreher and Karsten Gaenger. 2015. Key Performance Indicators and Measurements for LTE Radio Network Optimization. 267–336. https://doi.org/10.1002/9781118725092.ch4
  30. L. Huawei Technologies Co. 2006. HUAWEI RAN KPI for performance management. Technical Report.
  31. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, 95–104. https://doi.org/10.1145/3209978.3210006
  32. Radio Network Planning and Optimisation for UMTS. https://doi.org/10.1002/9780470031407
  33. The Good, the Bad, and the KPIs: How to Combine Performance Metrics to Better Capture Underperforming Sectors in Mobile Networks. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). 297–308. https://doi.org/10.1109/ICDE.2017.89
  34. Knowledge Adaption for Demand Prediction Based on Multi-Task Memory Neural Network. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). Association for Computing Machinery, New York, NY, USA, 715–724. https://doi.org/10.1145/3340531.3411965
  35. A measurement study on TCP behaviors in HSPA+ networks on high-speed rails. In 2015 IEEE Conference on Computer Communications (INFOCOM). 2731–2739. https://doi.org/10.1109/INFOCOM.2015.7218665
  36. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. CoRR abs/1707.01926 (2017). arXiv:1707.01926 http://arxiv.org/abs/1707.01926
  37. Multivariate Time Series Imputation with Generative Adversarial Networks. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 1603–1614.
  38. Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems 16, 2 (2015), 865–873. https://doi.org/10.1109/TITS.2014.2345663
  39. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors (Basel, Switzerland) 17 (2017).
  40. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies 54 (2015), 187–197. https://doi.org/10.1016/j.trc.2015.03.014
  41. Robust Assessment of Changes in Cellular Networks. In Proceedings of the Ninth ACM Conference on Emerging Networking Experiments and Technologies (Santa Barbara, California, USA) (CoNEXT ’13). Association for Computing Machinery, New York, NY, USA, 175–186. https://doi.org/10.1145/2535372.2535382
  42. R. Ajay Mishra. 2004. Fundamentals of Cellular Network Planning and Optimisation. John Wiley & Sons, Ltd. https://doi.org/10.1002/0470862696.ch10 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470862696.ch10
  43. Understanding Data Hotspots in Cellular Networks. IEEE. https://doi.org/10.4108/icst.qshine.2014.256291
  44. Nicholas G. Polson and Vadim O. Sokolov. 2017. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies 79 (2017), 1–17. https://doi.org/10.1016/j.trc.2017.02.024
  45. Deep State Space Models for Time Series Forecasting. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 7796–7805.
  46. Learning representations by back-propagating errors. Nature 323 (1986), 533–536.
  47. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting 36, 3 (2020), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
  48. Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). 259–270. https://doi.org/10.1109/ICDE.2017.85
  49. Alex Smola and Bernhard Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (2004), 199–222.
  50. Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies. Applied Mathematical Modelling 38, 2 (2014), 496–505. https://doi.org/10.1016/j.apm.2013.06.027
  51. Discovering Time-Dependent Shortest Path on Traffic Graph for Drivers towards Green Driving. J. Netw. Comput. Appl. 83, C (apr 2017), 204–212. https://doi.org/10.1016/j.jnca.2015.10.018
  52. Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. ArXiv abs/1911.10273 (2020).
  53. Y. K. Ting, Stephanie and T. Tiong Chai. 2008. WCDMA Network planning and optimisation. 317 – 322. https://doi.org/10.1109/NCTT.2008.4814295
  54. LTE Self-Organising Networks (SON): Network Management Automation for Operational Efficiency. 39–80. https://doi.org/10.1002/9781119961789.ch3
  55. Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks. In Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation (Lombard, IL) (nsdi’13). USENIX Association, USA, 459–472.
  56. MF-CNN: Traffic Flow Prediction Using Convolutional Neural Network and Multi-Features Fusion. IEICE Transactions on Information and Systems E102.D, 8 (2019), 1526–1536. https://doi.org/10.1587/transinf.2018EDP7330
  57. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2018/505
  58. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks. Sensors (Basel, Switzerland) 17 (2017).
  59. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems 21, 9 (sep 2020), 3848–3858. https://doi.org/10.1109/tits.2019.2935152
  60. LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems 11, 2 (2017), 68–75. https://doi.org/10.1049/iet-its.2016.0208 arXiv:https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2016.0208
  61. CNN-LSTM Based Traffic Prediction Using Spatial-temporal Features. Journal of Physics: Conference Series 2037, 1 (sep 2021), 012065. https://doi.org/10.1088/1742-6596/2037/1/012065
  62. Hotspots Prediction Based on LSTM Neural Network for Cellular Networks. Journal of Physics: Conference Series 1624 (10 2020), 052016. https://doi.org/10.1088/1742-6596/1624/5/052016
  63. Weiqing Zhuang and Yongbo Cao. 2022. Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information. Applied Sciences 12, 17 (2022). https://doi.org/10.3390/app12178714

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