Abstract
With the popularity of mobile access Internet and the higher bandwidth demand of mobile applications, user Quality of Experience (QoE) is particularly important. For bandwidth and delay sensitive applications, such as Video on Demand (VoD), Realtime Video Call, Games, etc., if the future bandwidth can be estimated in advance, it will greatly improve the user QoE. In this paper, we study realtime mobile bandwidth prediction in various mobile networking scenarios, such as subway and bus rides along different routes. The main method used is Long Short Term Memory (LSTM) recurrent neural network. In specific scenarios, LSTM achieves significant accuracy improvements over the state-of-the-art prediction algorithms, such as Recursive Least Squares (RLS). We further analyze the bandwidth patterns in different mobility scenarios using Multi-Scale Entropy (MSE) and discuss its connections to the achieved accuracy.
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- 1.
The collected NYU Metropolitan Mobile Bandwidth Trace Dataset (NYU-METS), is publicly available at https://github.com/NYU-METS/Main.
- 2.
We also tried a LSTM network with 256 and 256 nodes, and a LSTM network with 128 and 128 nodes. The performance difference is not significant. The results presented in this paper is based on the 256 + 128 LSTM network.
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Mei, L. et al. (2019). Realtime Mobile Bandwidth Prediction Using LSTM Neural Network. In: Choffnes, D., Barcellos, M. (eds) Passive and Active Measurement. PAM 2019. Lecture Notes in Computer Science(), vol 11419. Springer, Cham. https://doi.org/10.1007/978-3-030-15986-3_3
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DOI: https://doi.org/10.1007/978-3-030-15986-3_3
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