Mobile apps are increasingly relying on high-throughput and low-latency content delivery, while the available bandwidth on wireless access links is inherently time-varying. The handoffs between base stations and access modes due to user mobility present additional challenges to deliver a high level of user Quality-of-Experience (QoE). The ability to predict the available bandwidth and the upcoming handoffs will give applications valuable leeway to make proactive adjustments to avoid significant QoE degradation. In this paper, we explore the possibility and accuracy of realtime mobile bandwidth and handoff predictions in 4G/LTE and 5G networks. Towards this goal, we collect long consecutive traces with rich bandwidth, channel, and context information from public transportation systems. We develop Recurrent Neural Network models to mine the temporal patterns of bandwidth evolution in fixed-route mobility scenarios. Our models consistently outperform the conventional univariate and multivariate bandwidth prediction models. For the next second bandwidth prediction, in terms of Mean Absolute Error (MAE), our model is on average 15.28% better than the other methods in 4G traces and 15.37% better than the other methods in 5G traces. For 4G & 5G co-existing networks, we propose a new problem of handoff prediction between 4G and 5G, which is important to achieve good application performance in realistic 5G scenarios. We develop classification and regression based prediction models, which achieve more than 80% accuracy in predicting handoffs between 4G and 5G in a recent 5G dataset.
- Bandwidth prediction
- Deep learning
- Handoff prediction
- Public transportation scenario
ASJC Scopus subject areas
- Computer Networks and Communications