Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning

Falkenberg, Robert; Heimann, Karsten; Wietfeld, Christian

doi:10.1109/glocom.2017.8254567

Cited by 23 publications

(15 citation statements)

References 11 publications

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“…In future work, we want to further improve the transmission scheme by taking interdependencies with the transport layer protocols into account. Furthermore, we want to enhance the accuracy of network quality estimation by integrating knowledge about the cell capacity obtained from passive control channel analysis as described in [17].…”

Section: Discussionmentioning

confidence: 99%

Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

Sliwa¹,

Liebig²,

Falkenberg³

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.

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Section: Discussionmentioning

confidence: 99%

Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

Sliwa¹,

Liebig²,

Falkenberg³

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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“…Within the casestudy, about 95% of the overall traffic value was precisely predicted, which enabled the network operators to more than double the offered data rate using the optimization framework. 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.…”

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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“…An implementation and evaluation of such a jammer is detailed in Section IV. Given a DCI decoder, a target device's RBs can be found via its Radio Network Temporary Identifier (RNTI), obtainable by combining deanonymization [3] with tools like C3ACE [4].…”

Section: B Attack Vectorsmentioning

confidence: 99%

Towards Resilient 5G: Lessons Learned from Experimental Evaluations of LTE Uplink Jamming

Girke

Kurtz

Dorsch

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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Energy, water, health, transportation and emergency services act as backbones for our society. Aiming at high degrees of efficiency, these systems are increasingly automated, depending on communication systems. However, this makes these Critical Infrastructures prone to cyber attacks, resulting in data leaks, reduced performance or even total system failure. Beyond a survey of existing vulnerabilities, we provide an experimental evaluation of targeted uplink jamming against Long Term Evolution (LTE)'s air interface. Primarily, our implementations of smart attacks on the LTE Physical Uplink Control Channel (PUCCH), the Physical Uplink Shared Channel (PUSCH) as well as on the radio access procedure are outlined and tested. In exploiting the unencrypted resource assignment process, these attacks are able to target and jam specific UE resources, effectively denying uplink access. Evaluation results reveal the criticality of such attacks, severely destabilizing Critical Infrastructures, while minimizing attacker exposure. Finally we derive possible mitigations and recommendations for 5G stakeholders, which serve to improve the robustness of mission critical communications and enable the design of resilient next generation mobile networks.

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Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning

Cited by 23 publications

References 11 publications

Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

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

Towards Resilient 5G: Lessons Learned from Experimental Evaluations of LTE Uplink Jamming

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