CQI Maps for Optimized Data Distribution

Kelch, Lutz; Pögel, Tobias; Wolf, Lars; Sasse, Andreas

doi:10.1109/vtcfall.2013.6692148

Cited by 12 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, determining the most appropriate CQI value is necessary to maximize throughput. There have been several studies [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] on SIR or SINR (signal-to-interference plus noise ratio) and CQI in a HSDPA system, but only a few of them [25][26][27][28][29] focused on SIR-to-CQI mapping methods for a HSDPA system. In [25] SINR-to-CQI mapping is defined for an AWGN channel, and the linear relation between them is determined for a BLER of 0.1.…”

Section: Sir-to-cqi Mapping Methodsmentioning

confidence: 99%

A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

Kurnaz¹,

Engiz²,

Esenalp³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:This study aims to determine the mapping rule for signal-to-interference ratio (SIR) to channel quality indicator (CQI) based on DC-HSDPA real field measurements. The measurements were performed using the TEMS Investigation Tool at 85 different propagation mediums. The measurement results showed that the SIR-to-CQI mapping methods in the literature were insufficient to characterize actual radio environments; thus, proposal of a new empirical SIR-to-CQI mapping rule was aimed. This rule provides substantially better performance than the existing methods, and with this rule CQI can be generated from SIR with an accuracy of around 90% for DC-HSDPA systems.

show abstract

Section: Sir-to-cqi Mapping Methodsmentioning

confidence: 99%

A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

Kurnaz¹,

Engiz²,

Esenalp³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…One possibility is to use segments, which are already defined by the map provider. Such a model was proposed by Pögel and Wolf [82] as well as by Kelch et al [51]. One drawback of this approach is that road segments differ a lot in size.…”

Section: Location Smoothing Modelsmentioning

confidence: 99%

A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks

2021

View full text Add to dashboard Cite

Network communication has become a part of everyday life, and the interconnection among devices and people will increase even more in the future. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is quite a challenging task. In this survey, we provide an extensive insight into the literature on Transmission Control Protocol throughput prediction. The goal is to provide an overview of the used techniques and to elaborate on open aspects and white spots in this area. We assessed more than 35 approaches spanning from equation-based over various time smoothing to modern learning and location smoothing methods. In addition, different error functions for the evaluation of the approaches as well as publicly available recording tools and datasets are discussed. To conclude, we point out open challenges especially looking in the area of moving mobile network clients. The use of throughput prediction not only enables a more efficient use of the available bandwidth, the techniques shown in this work also result in more robust and stable communication.

show abstract

“…Maintaining network quality data: While the mobile UE is able to perform measurements of the network quality indicators at its current location itself, it has to rely on estimation methods for forecasting those indicators at future locations. For this purpose, connectivity maps [30], [31] can serve as a way for providing a data-driven method for maintaining geospatially-aggregated network quality information. In [3], connectivity maps are jointly used with mobility prediction in order to schedule the time of vehicular sensor data transmissions with respect to the expected network quality on the future route.…”

Section: Related Workmentioning

confidence: 99%

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

Sliwa

Wietfeld

2019

IEEE Access

View full text Add to dashboard Cite

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...

show abstract

CQI Maps for Optimized Data Distribution

Cited by 12 publications

References 9 publications

A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

A novel empirical SIR-to-CQI mapping rule for DC-HSDPA systems

A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks

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

Contact Info

Product

Resources

About