Although connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-complaint data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities.

Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the off-loading of large amounts of data can be more efficient than the local processing on a resource- constraint platform, while this latter solution is preferred in case of limited connectivity. This paper proposes a Client-Based Control Channel Analysis for Connectivity Estimation (C3ACE), a new passive probing mechanism to enable the client- side to estimate the connection quality of 4G networks in range. The results show that by observing and analyzing the control traffic in real-time, the number of active user equipment in a cell can be determined with surprising accuracy (with errors well below 10^-6). The specific challenge addressed in this paper lies in a dedicated filtering and validation of the DCI (Downlink Control Information). In a subsequent step the data rates to be expected can be estimated in order to enable decision about the choice of network and the timing of the data offloading to the cloud. The proposed methods have been implemented and validated leveraging the SDR OpenAirInterface, a real- life LTE network and a distributed load generator producing a scalable network traffic by a number of LTE User Equipment.

This contribution illustrates the benefit of incorporating real-time environmental data into highway traffic information systems and describes the modelling and integration of weather conditions into a complex microscopic traffic simulation. Using stationary measured weather data as an example, the achieved results show the potential extended Floating Car Data (xFCD) - submitted to the traffic information system by cellular communication - can have for traffic simulation. Therefore, an estimation about the expected communication network load is given, when xFCD equipped vehicles have become prevalent.

The load-aware operation of network infrastructure is a well established green networking concept to improve the energy-efficiency of wireless communication networks. But while it has been shown that switching-off base stations in times of low traffic leads to significant savings of energy, the interdependency of such infrastructure-oriented measures on the power consumption of user equipments has not been addressed in detail so far. This paper therefore quantizes the negative impact of switching-off strategies for base stations on the battery lifetime of Long Term Evolution (LTE) devices. Therefore, the Context- Aware Power Consumption Model for LTE devices (CoPoMo) that enables an accurate quantification of the energy efficiency of devices under different network deployments, is leveraged. In order to enable a trade-off between energy savings in the infrastructure and battery lifetimes of devices on the user side, we propose a Battery-Lifetime-Aware switching-off Strategy (BaLAnce) for base stations. The results of a simulation study with a reference LTE network scenario show, that a significantly better energy trade-off can be achieved by BaLAnce.

Many vehicular applications can be enabled of cellular communication networks like Long-Term Evolution (LTE). To guarantee a resource-efficient communication, the evaluation of the channel quality in the device is an important research topic in order to consider the channel quality for data transmission decisions. In this paper, the correlation between downlink channel quality indicators that are evaluated in the device and the uplink system performance is analyzed. For this purpose, theoretical analyses and field measurements in a dedicated LTE network as well as in a public LTE development are provided. The results show that the Reference Signal Received Power (RSRP) is suitable indicator for very good uplink connectivity situations and that the connectivity at the cell edge can be identified by the Reference Signal Received Quality (RSRQ).

The prediction of both, vehicular traffic and communication connectivity are important research topics. In this paper, we propose the usage of innovative machine learning approaches for these objectives. For this purpose, Poisson Dependency Networks (PDNs) are introduced to enhance the prediction quality of vehicular traffic flows. The machine learning model is fitted based on empirical vehicular traffic data. The results show that PDNs enable a significantly better short-term prediction in comparison to a prediction based on the physics of traffic. To combine vehicular traffic with cellular communication networks, a correlation between connectivity indicators and vehicular traffic flow is shown based on measurement results. This relationship is leveraged by means of Poisson regression trees in both directions, and hence, enabling the prediction of both types of network utilization.

Due to continuously growing communication traffic of emerging mobile phone applications, resource-efficient communication is a key to affordable network services with high Quality of Experience. While some communication traffic requires immediate resource allocations (such as voice services), an increasing number of mobile phone applications produce a lot of background communication traffic (e.g. social network apps, crowd sensing services). In this paper we discuss the predictive Channel-Aware Transmission (pCAT) scheme, which leverages the fact, that favorable channel conditions require much less spectrum resource allocation than bad channel conditions. By leveraging the knowledge of user trajectories and recurring spots with favorable channel conditions, the so-called LTE connectivity hot spots, background traffic transmissions can be scheduled by the client according to expected channel quality and application data priority. Thereby, the spectrum consumption of background traffic of applications can be reduced significantly. At the same time, the efficient usage of spectrum resources has also an impact on the battery lifetime of the mobile devices. By introducing the Context-Aware Power Consumption Model (CoPoMo) for LTE communications, we highlight, how decisions about the spectrum resource allocation by the network will impact the battery lifetime. One case study will show, that by simply changing the resource allocation scheme and without the need for spending more spectrum resources, the power consumption can be reduced by more than 70% using the Energy-Efficient Scheduling (EES) introduced in this paper.

Extended Floating Car Data (xFCD) is the fundamental for many modern traffic forecast systems. In this paper, an xFCD source model with different transmission rules for making the data efficiently available at the forecast server by means of Long Term Evolution (LTE) enabled Machine-Type Communication (MTC) is proposed. Thereby, the delay requirements of the sensor data and the channel conditions of the communication link are taken into account. The results show that the new source model leads to a decreased utilization of the LTE system and therefore to a low service degradation of other human cellular users. Furthermore, the inserted delay of the data transmission is tolerable for the considered application.

In Long Term Evolution (LTE), Voice over Internet Protocol (VoIP) is used for the delivery of the voice data. One of the major challenges for VoIP over LTE is to guarantee the Quality of Experience (QoE) of the VoIP calls. This paper uses an LTE base station emulator and a radio channel emulator to evaluate the impacts of different User Equipment (UE) velocities and Radio Link Control (RLC) parameter values on the QoE of the calls in the vehicular environment. Our study shows that LTE can provide acceptable VoIP quality in the high-speed scenarios (e.g., high-speed train). This paper provides the guidelines for the telecommunications operators to select the appropriate parameter values when deploying their VoIP services in LTE.

System level analytical models and simulations usually use analytical propagation models for estimating received signal power. These propagation models are typically based on statistical properties. Consequently, they are very well suited for general assessment of communication technologies and protocols. However, their universality lets them produce unrealistic results in situations where the concrete assessment of network situations is needed. This type of assessment is particularly useful for network self-healing mechanisms, e.g relay positioning. For this purpose, ray tracing based approaches provide detailed assessment of real network configurations. Ray tracing can thus be used for replacing the path loss estimating part in analytical system level Long Term Evolution (LTE) models or simulations. In this work, a novel combination of ray tracing and an existing analytical interference-aware downlink model was made possible through extension of the model. It is shown at the example of a realistic scenario that system level interference control can be used to enhance the overall user satisfaction of the LTE system. This is done through prevention of a full interference situation by throttling non-premium users.

One important advantage of the Orthogonal Frequency Division Multiple Access (OFDMA) based cellular communication system Long Term Evolution (LTE) is its robustness against different kinds of impairments as they may occur on the mobile radio communication channel. This includes an increased toughness not only in harsh multi path scenarios, but also for the case of extremely high user velocities. In this paper, the applicability of the LTE system for two example applications (LTE based backhaul for high speed trains and live transmission of flight recorder data) is investigated by means of extensive throughput measurements (TCP and UDP) incorporating a radio channel emulator. The measurement results show that LTE in general is capable to provide reliable communication links for both considered scenarios. However, extreme velocities as they occur in a “flight data transmission at cruising speed” scenario rely on a specialized cross-layer parameterization.

The interaction between Machine-to-Machine (M2M) Communications, also known as Machine-Type Communications (MTC), and Human-to-Human (H2H) Communications is a recent topic in the context of cellular networks like Long Term Evolution (LTE). In this paper, we evaluate the performance of Channel-Aware MTC (CAT) based on LTE resource allocation measurements of a real LTE deployment. By means of a novel measurement setup, which uses a real-time spectrum analyzer, the LTE resource utilization in terms of occupied Resource Blocks (RBs) of the uplink is quantified for different channel conditions. Furthermore, the resource distribution in time and frequency domain, which is determined by the scheduler, is analyzed in this paper allowing for the calculation of average data rates and the spectral efficiency. The measurement results are used to quantize the potential gain of CAT. It is shown that by means of the scheme, the influence of MTC on other LTE users can be significantly reduced. In addition, the fraction of available RBs can be increased by 47%.

Machine-Type Communication (MTC) poses an ongoing research topic in the development of cellular communication systems. In this context, the efficient collection of extended Floating Car Data (xFCD) via Long Term Evolution (LTE) is a major challenge. In this paper, we present cluster-based xFCD collection schemes in order to form clusters with a long lifetime. As a result, the proposed clustering algorithms reduce the occurring cellular communication traffic. For the performance evaluation of the presented algorithm, a novel system model is used. By means of the system model, the user mobility can be modeled realistically and a precise quantification of the utilization of the LTE network for xFCD transmission is possible. The results show that the LTE network utilization can be significantly reduced by the proposed clustering algorithms.

The average operational time of today's smart phones with one filling of the accumulator is one of the most important performance parameters for the customers of new devices. Nevertheless, this value is remaining constant or even decreasing in the last few years due to the continuously increasing complexity. On the other hand, for ensuring the fulfillment of the high user-requirements even under worst case conditions, novel cellular systems such as Long Term Evolution (LTE) are usually over-dimensioned in terms of the maximum available capacity. In this paper, a novel approach is presented which allows for trading in unused cell capacity for battery lifetime extension of the mobile devices. The basic idea in this context is to increase the robustness of the submitted uplink (UL) signal by decreasing the order of the modulation and coding scheme used. If the provided data rate is kept constant, this comes along with an increasing number of allocated Resource Blocks (RB) and therefore a decreased overall cell capacity. Nevertheless, the more robust signal allows for reducing the uplink transmit power while the Quality of Service (QoS) parameters are still met. The results for exemplary Voice over IP (VoIP) traffic show that for users originally submitting at the maximum available transmit power, the consumed energy of the LTE chip-set can be reduced by up to 27.5 % applying this novel approach.

In this paper, we present a channel sensitive transmission scheme which reduces the negative impact of Vehicle-to-Infrastructure (V2I) traffic on the Quality of Services (QoS) of Human to Human (H2H) communication in cellular networks. The performance evaluation of Long Term Evolution (LTE) for different traffic characteristics is based on an introduced Markovian model. This describes the utilization of the shared LTE Resource Blocks (RBs). The model is parameterized by laboratory measurements and ray tracing simulations. We present blocking probabilities for an LTE network with heterogeneous V2I and H2H traffic and propose different transmission strategies for V2I data with the goal to minimize the impact on human users. The results show that the number of V2I devices can be doubled by using channel sensitive transmission schemes ensuring equal QoS for H2H communication compared to periodically data transmission.

In this paper, an energy efficient approach for the transmission of Floating Car Data (FCD) via Long Term Evolution (LTE) is presented. Applying channel sensitive transmission, FCD is collected by mobile devices in cars and transmitted preferably if the channel conditions are good. The results show that this approach reduces the negative impact on the utilization of the LTE air interface as well as on the energy consumption of the mobile devices. For the performance evaluation of the LTE system, a close to reality parametrized Markovian model is used. The parameters of the model are determined by laboratory measurements and ray tracing simulations. In the laboratory, also the energy consumption of the used LTE USB stick is measured. Hence, we evaluated that the channel sensitive transmission allows for an energy saving of up to 54 % for the transmission of the FCD.

The energy that has to be spent for the successful submission of one Bit is an important figure of merit for the performance analysis and optimization of modern wireless communication systems. The many factors which are influencing this performance parameter range from the efficiency of the User Equipment's (UE) power amplifier to the average path loss, the Transmit Power Control (TPC) parametrization and the fading characteristics of the radio channel. Although many of the relationships can be analytically modeled, the aim of this paper is to present reliable measurements based on commercially available Long Term Evolution (LTE) hardware. Therefore, extensive User Datagram Protocol (UDP) data rate measurements have been performed in a mobile communications laboratory for different radio channel conditions. The impact of the mobile fading channel was emulated by a sophisticated radio channel emulator. Beside this, the on average consumed power of the LTE UE was measured during data transmission. From the results of these measurements, quantitative figures on the energy efficiency are presented for different LTE frequency bands and different radio channels. The results show that major energy savings are possible if the 800 MHz frequency band, which becomes available as part of the digital dividend, can be used for user with bad channel conditions. Considering energy efficiency as a Quality of Service (QoS) parameter of increasing importance the results presented in this paper allow for a context sensitive optimization in a way that the Modulation and Coding Scheme (MCS) switching points as well as the frequency band are chosen with respect to the UE's condition.

The operational time of modern smart-phones with one filling of the accumulator is one of the most important performance parameter for the customers of new devices. This is the reason why extensive research has been performed in the fields of battery design and system optimization with regard on energy consumption. Due to the fact that the radio part of a smart-phone is one of the major energy consumers, a special focus has to be set on different approaches and strategies how to reduce the energy that has to be spent for the transmission of a certain amount of data. However, before new energy saving protocols can be efficiently developed one needs to have detailed knowledge on the different factors which impact the energy efficiency. In this paper, we present a measurement based energy model for IEEE 802.16e conform Mobile WiMAX devices. For this purpose, extensive measurements of the energy consumption of a Mobile WiMAX USB Stick have been performed for different system parameterizations, different data sizes and different application data rates. From the results of the measurements analytic models have been derived which allow for the calculation of the energy that has to be spent per successfully submitted bit. These analytic formulas can now be integrated in system level simulators for the evaluation of the energy efficiency of newly designed protocols.

The travel time estimation of vehicles is a major challenge in the area of dynamic traffic prognosis. Our approach is to increase the number of considered sensor objects in the road network. For this purpose Floating Car Data (FCD) including travel time information of vehicles is transmitted to a server via Long Term Evolution (LTE). In this paper, the benefit of FCD on the accuracy of travel time estimation, depending on the FCD penetration rate is analyzed by an enhanced Nagel-Schreckenberg cellular automaton model. Furthermore, the negative impact of the FCD transmission on the air interface of the cellular communication system is evaluated for various penetration rates and different transmission strategies, including a channel sensitive transmission. Therefore, a close to reality parameterized Markovian model is used. The results show that a penetration rate of a few percent is sufficient for a realistic travel time estimation. The respective influence on the LTE network is tolerable, especially for channel sensitive transmission.

The performance evaluation of mobile communication systems for time varying environments poses a major challenge. To address this issue, in this paper we propose an approach which extends a common laboratory environment by a fading channel emulator. Hereby, we analyze OFDM based links under complex and realistic radio channel conditions including upper layer protocols. By means of this setup, the influence of velocity on the data rate and Packet Error Rate (PER) of a Mobile WiMAX system is investigated for various Signal to Noise Ratios (SNR) assuming vehicular and pedestrian channel models defined by the ITU. As a result, we analyzed the performance of Mobile WiMAX for Adaptive Modulation and Coding (AMC) dependent on the QoS target, the channel model, the user velocity and the SNR. Hereby, we assumed two partially contrary QoS targets, high data rate and target PER.