UE-Based Estimation of Available Uplink Data Rates in Cellular Networks

Nikolov, Georgi; Kühn, Michael; Wenning, Bernd-Ludwig

doi:10.1109/wimob.2018.8589097

Cited by 5 publications

(9 citation statements)

References 7 publications

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“…As it was shown in [6], accurately modelling the uplink data rate from channel quality indicators using mathematical functions is not feasible. Therefore, choosing a data driven approach makes sense.…”

Section: Use Case and Motivationmentioning

confidence: 99%

“…To the best of our knowledge there is no efficient online training algorithm for RF regressions. In [6] we tweaked the objective function of a NN to achieve better predictions in lower data rates. As we describe in the next section, efficient and quick online training of NNs is not possible with the current state of the art.…”

Section: A Uplink Data Rate Estimationmentioning

confidence: 99%

“…linear regression, Support-Vector Regression). Linear regression can be used to roughly approximate the relationship between channel quality indicators and uplink data rate [6]. Another approach is the Support-Vector Regression.…”

Section: B Relevant Machine Learning Algorithmsmentioning

confidence: 99%

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

Nikolov

Kühn

McGibney

et al. 2020

IEEE J. Select. Areas Commun.

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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.

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Section: Use Case and Motivationmentioning

confidence: 99%

Section: A Uplink Data Rate Estimationmentioning

confidence: 99%

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

Nikolov

Kühn

McGibney

et al. 2020

IEEE J. Select. Areas Commun.

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“…Recent research estimated available throughput using machine learning technology [20][21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

“…Nikolov et al passively estimates an available uplink throughput on universal mobile telecommunications system (UMTS) and LTE networks using a neural network [24]. Machine learning is a powerful tool for estimating available throughput.…”

Section: Related Workmentioning

confidence: 99%

Passive Method for Estimating Available Throughput for Autonomous Off-Peak Data Transfer

Satoda

Takahashi

Onishi

et al. 2020

Wireless Communications and Mobile Computing

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Large demands for mobile traffic subject base stations to frequent short-term and sharp peak loads. Recent analysis of data traffic on commercial mobile networks reported that the traffic peaks can be reduced by an average of 40% without compromising the quality of experience provided to the end user, if a peak load can be shifted for at most 20 s. To reduce peak traffic, we previously proposed a method for off-peak data transfer, with which user equipment (UE) autonomously delays receiving data, and a peak load on a base station can be shifted. In terms of off-peak transfer of data, a significant problem is determining how each UE estimates available throughput. In this paper we propose a method of passively estimating available throughput of each UE. We evaluated the effectiveness of the proposed method through experiments on experimental and commercial LTE networks. e results indicate that our method obtains more than a 0.7 correlation between actual available throughput and estimated throughput.

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Data-Driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

Sliwa

Wietfeld

2019

IEEE Access

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IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, collecting new collected works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Abstract-The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively considering the properties of the highly dynamic radio channel within the communication systems themselves. Since real world experiments are highly time-consuming and lack a controllable environment, performance evaluations and parameter studies for novel anticipatory vehicular communication systems are typically carried out based on network simulations. However, due to the required simplifications and the wide range of unknown parameters (e.g., Mobile Network Operator (MNO)-specific configurations of the network infrastructure), the achieved results often differ significantly from the behavior in real world evaluations. In this paper, we present Data-driven Network Simulation (DDNS) as a novel data-driven approach for analyzing and optimizing anticipatory vehicular communication systems. Different machine learning models are combined for achieving a close to reality representation of the analyzed system's behavior. In a proof of concept evaluation focusing on opportunistic vehicular data transfer, the proposed method is validated against field measurements and system-level network simulation. In contrast to the latter, DDNS does not only provide massively faster result generation, it also achieves a significantly better representation of the real world behavior due to implicit consideration of crosslayer dependencies by the machine learning approach.The authors are with Communication Networks Institute, TU Dortmund University, be brought to the next performance level. The exploitation of machine learning offers the potential to be the catalyst for this development [5], as its inherent strength is to leverage hidden interdependencies between measurable variables, which are mostly too complex to be covered in an analytical solution.The development process of these novel anticipatory vehicular communication systems confronts researchers and engineers with a methodological dilemma: While the most accurate estimations for the future real world performance can be achieved by performing real world experiments, this approach is highly time consuming and lacks a controllable environment. In fact, it is practically impossible to guarantee fairness by evaluating different methods under the exact same network conditions. System-level network simulation based on Discrete Event Simulation (DES) has emerged as the most commonly used scientific method to analyze mobile communication systems [6], due to its capability of solving both issues. However, the necessary model simplifications reduce the significance of...

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UE-Based Estimation of Available Uplink Data Rates in Cellular Networks

Cited by 5 publications

References 7 publications

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

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

Passive Method for Estimating Available Throughput for Autonomous Off-Peak Data Transfer

Data-Driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

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