Correlation-Based Feature Mapping of Crowdsourced LTE Data

Apajalahti, Kasper; Walelgne, Ermias Andargie; Manner, Jukka; Hyvönen, Eero

doi:10.1109/pimrc.2018.8580999

Cited by 5 publications

(8 citation statements)

References 103 publications

(190 reference statements)

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“…Crowdsourced datasets are useful for the analysis and exploration of the MNOs' performances. There are numerous works employing different aspects of crowdsourced data for better network optimization in MNOs [17,18]. Using Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR) Open Data (a crowdsourced dataset) 2 , Kousias et al [17] investigated the effect of different features that distinguished MNOs from each other.…”

Section: Crowdsourced Datamentioning

confidence: 99%

Reliability comparisons of mobile network operators: an experimental case study from a crowdsourced dataset

Zeydan¹,

Yıldırım²

2020

Turk J Elec Eng & Comp Sci

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It is of great interest for Mobile Network Operators (MNOs) to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss (PL) performance comparisons of MNOs using a previously collected real-world proprietary crowdsourced dataset from a user application for 13 months' duration in Turkey. More particularly, a unified crowdsourced data-aided statistical MNO comparison framework is proposed, which consists of data collection and network performance analysis steps. Our results are statistically supported using confidence interval analysis for the mean difference of PL ratios and reliability levels of MNOs using unpaired number of observations statistical analysis. The network performance results indicate that significant performance differences in MNOs depending on different regions of the country exist. Moreover, we observe that the overall comparative ordered list of MNOs' reliability performance does not differ when both PL and latency requirements vary.

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Section: Crowdsourced Datamentioning

confidence: 99%

Reliability comparisons of mobile network operators: an experimental case study from a crowdsourced dataset

Zeydan¹,

Yıldırım²

2020

Turk J Elec Eng & Comp Sci

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“…Although it is possible to use and maintain these data bases in a completely decentralized way -as people often drive the same routes regularly -data freshness and the grade of covered areas can be significantly increased through exploitation of crowdsensing approaches [32]. In order to increase the overall knowledge data base through using potentially heterogeneous data from different sources, correlationbased feature mapping [33] can be applied. As an alternative to purely measurement-based approaches, the acquired data can be exploited to optimize the parameterization of radio propagation models.…”

Section: Related Workmentioning

confidence: 99%

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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“…Several works explore the use of crowdsourced parameters measured by dedicated mobile apps to characterize the UE downlink/uplink throughput [22]- [31]. In particular, the candidate metrics include the Reference Signal Received Power (RSRP) [24], [25], [27]- [30], the Reference Signal Received Quality (RSRQ) [24], [27], [29], [30], implementation-specific signal strength metrics [22], [23], [27], [31], the Received Signal Strength Indication (RSSI) [25], the Signal to Noise and Interference Ratio (SINR) or Signal to Noise Ratio (SNR) [25], the Reference Signal Signal to Noise Ratio (RSSNR) [24], [27], and downlink or uplink data rate measurements obtained with active probes [22], [23], [26], [27], [31]. In the following, we provide more details about the outcomes of [22]- [31] which are relevant to our work.…”

Section: ) Qos Characterizationmentioning

confidence: 99%

“…Walelgne et al [26] analyze key features of mobile network related to the throughput, pointing out that this metric depends on a large variety of factors, such as the adopted radio technology, the physical layer effects, the UE demand and mobility, as well as the mobile infrastructure in use. Apajalahti et al [27] study the correlation of different metrics obtained from crowdsourced mobile data, including: uplink throughput, downlink throughput, RSRP, and RSRQ. The metrics are extracted from the NetRadar and the RTR Nettest platforms.…”

Section: ) Qos Characterizationmentioning

confidence: 99%

“…The metrics are extracted from the NetRadar and the RTR Nettest platforms. Interestingly, both the Pearson's correlations and the Spearman's correlations reveal that there is a not negligible correlation between the RSRP and the uplink throughput (see [27,). Enami et al [28] build a tool, called RAIK, which is able to predict Key Performance Indicators (KPIs) such the RSRP and the path loss by applying an approach based on neural networks.…”

Section: ) Qos Characterizationmentioning

confidence: 99%

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Not in My Neighborhood: A User Equipment Perspective of Cellular Planning Under Restrictive EMF Limits

et al. 2019

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The installation of base station (BS) sites is regulated by a variety of laws at international, national, and local levels. While international regulations are already severe, the national and local laws applied in many countries and regions follow precautionary principles and enforce electromagnetic field (EMF) constraints that are even more restrictive. This legal environment results in substantial constraints affecting the planning of cellular networks, as requests for new BS site installation are easily denied by national or local authorities. In this paper, we consider the problem of cellular planning under restrictive EMF limits from the user equipment (UE) viewpoint. We focus on outdoor urban areas and first evaluate the impact of the current, non-optimal network planning at the UE side through a quantitative measurement-driven analysis of the quality of service (QoS) observed by users in heterogeneous, large-scale urban scenarios. We then perform a qualitative assessment of the perceived QoS and generated EMF levels at one UE transferring data from/to a BS based on its position with respect to the serving BS. Finally, we run a what-if analysis by comparing the existing planning with the one where new BS sites can be installed, thanks to a relaxation of the restrictive EMF constraints. Our results clearly show that a cellular planning driven by restrictive EMF constraints forces UE to experience large distances from the serving BS, frequent non-lineof-sight conditions, and poor received signal. In turn, this entails a very negative combination of high electric field activity (EFA) levels generated by the UE and low QoS perceived by the user. We show that, by relaxing the restrictive EMF constraints, the problem could be sensibly mitigated with a positive impact on the UE channel conditions and consequently on the perceived QoS and the UE EFA. INDEX TERMS Cellular network planning, EMF limits, impact on QoS, user equipment measurements.

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Correlation-Based Feature Mapping of Crowdsourced LTE Data

Cited by 5 publications

References 103 publications

Reliability comparisons of mobile network operators: an experimental case study from a crowdsourced dataset

Reliability comparisons of mobile network operators: an experimental case study from a crowdsourced dataset

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

Not in My Neighborhood: A User Equipment Perspective of Cellular Planning Under Restrictive EMF Limits

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