4G LTE on the Road - What Impacts Download Speeds Most?

Akselrod, Mark; Becker, Nico; Fidler, Markus; Luebben, Ralf

doi:10.1109/vtcfall.2017.8288296

Cited by 25 publications

(16 citation statements)

References 14 publications

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“…Interestingly, the RSRP varies substantially across the scenarios and across the operators, with pixels in which the measured RSRP is pretty high (i.e., ≥80 [dBm]) and other ones instead in which the RSRP is very low (i.e., ≤−110 [dBm]). We point out that the RSRP spatial variation has been also observed by previous works in other scenarios (see [24], [25], [29], [30]), and our analysis confirms this behavior. By observing each scenario in detail, we remark that TIM and Wind Tre seem to provide higher values of RSRP compared to Vodafone in the TMC district (which is inline with the QoS evaluation outcome of [19]).…”

Section: Rsrp Characterizationsupporting

confidence: 90%

“…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%

“…Akselrod et al [25] evaluate the main factors impacting the UE download rate in LTE networks, observing that the signal quality, which depends on the UE position, has the highest impact. However, this metric is also affected by the frequency band and the bandwidth adopted by the operator.…”

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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Section: Rsrp Characterizationsupporting

confidence: 90%

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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“…While theoretically, the resulting data rate of a transmission is the result of a deterministic process, predicting those values proactively within a live-system is a challenging task due to the large number of involved hidden influences (e.g., scheduling, packet loss, channel stability, spectrum sharing and cross-layer interdependencies) [28]. Different authors have investigated the impact of the channel quality to the resulting data rate of cellular data transmissions [15], [29], [30], [31], [32] in different environments that range from highway to inner city scenarios. The studies agree that passively measurable network quality indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signalto-interference-plus-noise Ratio (SINR), and Channel Quality Indicator (CQI) provide meaningful information, which can be leveraged to estimate the resulting data rate based on machine learning methods even in challenging environments.…”

Section: Related Workmentioning

confidence: 99%

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Sliwa

Falkenberg

Liebig

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure.With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learningenabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%. Index Terms-Context-predictive Communication, Machine Learning, Crowdsensing, Intelligent Transportation Systems, Mobile Sensors {Nico.Piatkowski, Thomas.Liebig}@tu-dortmund.dedata packets. However, this technology is not able to provide internet-based vehicle-to-cloud connectivity, as there are practically no deployments of Roadside Units (RSUs), which offer the required gateway functionalities. Therefore, delay-tolerant and data-intense messaging is intended to be carried out based on existing cellular communication technologies (e.g., LTE and upcoming 5G networks), which already offer large-scale coverage. With the expected massive increase in vehicular MTC [7] and the general growth of cellular data traffic [8], the network infrastructure is facing the challenge of resourcecompetition between human cell users and Internet of Things (IoT)-related data transmissions [9]. Fig. 1 gives an overview about the requirements of different vehicular and IoT-based communication systems and the resulting challenges that arise from the channel dynamics and the limited cell resources.A promising approach to address these issues is the application of context-aware communication [10] that exploits the dynamics of the communication channel to schedule delaytolerant transmissions in an opportunistic way for increasing the transmission efficiency with regard to data rate, packet loss probability and energy consumption. As a consequence, communication resources are occupied for shorter time intervals and can early be used by other cell users, which enables a better coexistence and overall system performance [11].In this paper, we extend and bring together the methods, results and insights of previous work [12], [13], [14], [15], [16] on context-aware car-to-cloud communication and propose a client-side opportunistic transmission scheme that applies Energy Efficiency Application Requirements Latency Vehicle-as-a-sensor

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“…Candidate task to be allocated framework as well as the parameters and constraints for the edge and hub devices and the cloud unit. Furthermore, we ran our framework twice for each configuration, to reflect for any bandwidth variations, depending on where the application is deployed, e.g., rural, urban, and the distance between the nodes [1]. During the first run the bandwidths of the communication channels Hub − → Cloud and Cloud − → Hub are set at 2.0× compared to the second run.…”

Section: Image Preproccesingmentioning

confidence: 99%

Metis

Kouloumpris

Theocharides

Michael

2019

Proceedings of the International Conference on Omni-Layer Intelligent Systems

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Increased demands for real-time decision support and data analytics facilitate the need of performing significant computing away from the cloud and onto the IoT devices. In this paper, we propose Metis, a mathematical programming based framework, able to deliver an optimal task allocation when targeting a specific performance metric. Metis is currently suitable for systems which consist of an edge node, an intermediate node and the cloud infrastructure, but can be expanded to multi-Edge/Hub systems. Evaluation results using a real-life use-case scenario demonstrate that Metis provides the optimal task allocation by minimizing the overall latency of the system while taking into consideration the application's specific requirements and resource constraints of each computational unit.

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4G LTE on the Road - What Impacts Download Speeds Most?

Cited by 25 publications

References 14 publications

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

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

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Metis

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