ASR - Adaptive Similarity-Based Regressor for Uplink Data Rate Estimation in Mobile Networks

Abstract

This paper presents a passive data rate estimation method that leverages commonly available parameters of commercial modems with application in Intelligent Transportation Systems. The estimation is performed by utilizing an Adaptive Similarity-based Regression (ASR) approach. This constitutes the use of Support-Vector Regression (SVR) in conjunction with a similarity-based unlearning algorithm. It is demonstrated that this approach can adapt to the various properties of different mobile networks, while maintaining a fixed training set size. This is particularly useful in cases where training data is not available in large quantities, or the uplink rate is limited by the users subscription. ASR is developed as a set of modular components and as such can be used as an enhancement to protocols such as multipath Transmission Control Protocol (TCP), Software-Defined Networking (SDN), or as an aid to Quality-of-Service (QoS) routing. It is shown that the algorithm can achieve satisfactory performance with as little as 24 training samples, and can be deployed across different mobile networks without the need of pre-training. The solution is validated using a custom test-bed to perform mobile network measurements, gathering over 15, 000 measurement samples. In addition, the algorithm is tested using measurements collected under real life conditions both in a moving car and train. As there is a shortage of open source data in the field of rate estimation in mobile networks, we publish all of the data sets used in this paper to encourage further research on the subject.