Realtime Mobile Bandwidth and Handoff Predictions in 4G/5G Networks

TL;DR

This paper investigates real-time prediction of bandwidth and handoffs in 4G/5G networks using RNN models, demonstrating improved accuracy and addressing the challenge of proactive QoE management in mobile applications.

Contribution

It introduces RNN-based models for bandwidth and handoff prediction in 4G/5G networks, including a novel handoff prediction problem with high accuracy.

Findings

RNN models outperform traditional bandwidth prediction methods.

Achieved over 80% accuracy in 4G/5G handoff prediction.

Collected rich mobility and network data from public transportation.

Abstract

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 4G \& 5G co-existing networks, we propose a new problem of handoff prediction between 4G and 5G, which is important for low-latency applications like self-driving strategy in realistic 5G scenarios. We develop classification and regression based prediction models, which achieve more than 80\% accuracy in predicting 4G and 5G handoffs in a recent 5G dataset.