Towards Data-driven Simulation of End-to-end Network Performance Indicators

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations

Performance Analysis of Unsupervised LTE Device-to-Device (D2D) Communication

Cellular network technology based device-to-device communication attracts increasing attention for use cases such as the control of autonomous vehicles on the ground and in the air. LTE provides device-to-device communication options, however, the configuration options are manifold (leading to 150+ possible combinations) and therefore the ideal combination of parameters is hard to find. Depending on the use case, either throughput, reliability or latency constraints may be the primary concern of the service provider. In this work we analyze the impact of different configuration settings of unsupervised LTE device-to-device (sidelink) communication on the system performance. Using a simulative approach we vary the length of the PSCCH period and the number of PSCCH subframes and determine the impact of different combinations of those parameters on the resulting latency, reliability and the interarrival times of the received packets. Furthermore we examine the system limitations by a scalability analysis. In this context, we propose a modified HARQ process to mitigate scalability constraints. Our results show that the proposed reduced HARQ retransmission probability can increase the system performance regarding latency and interarrival times as well as the packet transmission reliability for higher channel utilization

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

To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobile device will only see what it obtains from the network once it actively sends data. A passive estimation in advance is therefore not doable without further effort. Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users. To close this gap we present an Enhanced Client-based Control-Channel Analysis for Connectivity Estimation (EC3ACE), which uncovers the cell load broken down by each single user. Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link. Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases

Integrated PMR-Broadband-IP Network for Secure Real-time Multimedia Information Sharing

This article appeared in Homeland Security Affairs (May 2012), supplement 5, article 3"In this paper, the authors present a novel solution for the integration of TETRA-based [Terrestrial Trunked Radio] PMR [Professional Mobile Radio] and IP [Internet Protocol] based wireless broadband networks through a novel inter-system interface. This solution enables secure group communications based on PMR standards using heterogeneous devices ranging from a traditional PMR device to smart phones such as the iPhone. Thereby a Smart-phone user will be enabled to leverage on one hand the multimedia data capabilities of 3G and 4G wireless networks (UMTS [Universal Mobile Telecommunications System], LTE [Long Term Evolution]) while at the same time be part of a PMR group communication. In other words, any authorized Smart-phone can become part of a PMR communication group by simply downloading the appropriate, dedicated Application. As a key benefit, homeland security personnel can be included in the disaster response actions instantaneously, without necessarily carrying around a PMR device and without the need for PMR coverage. In contrast to existing solutions, the proposed interface solution prevents the reduction of the voice quality when bridging system boundaries by tandem encoding with a TETRA-over-IP (ToIP) interconnection. The presented solutions include different interconnection setups including Trunked Mode (TMO) and Direct Mode (DMO) capabilities. To enable the group communications services as known in PMR systems, a dedicated protocol, the Push-to-X protocol developed by CNI [Communication Networks Institute], is leveraged. The results of performance evaluations show that the speech quality is still acceptable even under harsh conditions. The proposed system therefore paves the way towards a future, high performance PMR based on LTE, while preserving backwards compatibility with existing PMR systems.

System-in-the-loop Design Space Exploration for Efficient Communication in Large-scale IoT-based Warehouse Systems

Instead of treating inventory items as static resources, future intelligent warehouses will transcend containers to Cyber Physical Systems (CPS) that actively and autonomously participate in the optimization of the logistical processes. Consequently, new challenges that are system-immanent for the massive Internet of Things (IoT) context, such as channel access in a shared communication medium, have to be addressed. In this paper, we present a multi-methodological system model that brings together testbed experiments for measuring real hardware properties and simulative evaluations for large-scale considerations. As an example case study, we will particularly focus on parametrization of the 802.15.4-based radio communication system, which has to be energy-efficient due to scarce amount of harvested energy, but avoid latencies for the maintenance of scalability of the overlaying warehouse system. The results show, that a modification of the initial backoff time can lead to both, energy and time savings in the order of 50% compared to the standard

Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model

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

Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability