Geometry-Based Stochastic Channel Model for Train-to-Train Communication in Open Field Environment

Future railway applications, e.g., wireless train bus or virtual coupled trains, will rely on train-to-train (T2T) communication. Those future applications require an exchange of safety critical data between trains within one train-set based on wireless communication. Hence, the investigation of the propagation effects and the influence on the wireless communication is tremendously important. Future developments of communication standards and systems demand a detailed characterization and modeling. We investigated the propagation mechanisms based on channel sounding measurements and derived statistics for the propagation effects. Based on the environment, geometry and the propagation statistics we derive channel models for T2T communication. To cope with the movement of the trains, the changing environment and resulting temporal correlation effects we present a geometry-based stochastic channel model (GSCM) for T2T communication in open field environment

Wide Band Propagation in Train-to-Train Scenarios - Measurement Campaign and First Results

Within the next decades the railway systems will change to fully autonomous high speed trains (HSTs). An increase in efficiency and safety and a reduction of costs would go hand in hand. Today’s centralized railway management system and established regulations can not cope with trains driving within the absolute braking distance as it would be necessary for electronic coupling or platooning maneuvers. Hence, to ensure safety and reliability, new applications and changes in the train control and management are necessary. Such changes demand new reliable control communication links between train-to-train (T2T) and future developments on train-to-ground (T2G). T2G will be covered by long term evolution-railway (LTE-R) which shall replace today’s global system for mobile communications-railway (GSM-R). The decentralized T2T communication is hardly investigated and no technology has been selected. This publication focuses on the wide band propagation for T2T scenarios and describes a extensive channel sounding measurement campaign with two HSTs. First results of T2T communication at high speed conditions in different environments are presented

Path Loss Models and Large Scale Fading Statistics for C-Band Train-to-Train Communication

The profound knowledge of wireless propagation is essential for wireless communication between vehicles. To evolve and test communication standards we need channel models in representative environments to neither over-, nor underestimate the effect of the surrounding environment and the movement of the vehicles; typical environments for railway communication are railway station, open field and hilly environments. We introduce train-to-train (T2T) path loss models and large scale fading statistics based on channel sounder measurement data as a first step towards a geometry-based stochastic channel model (GSCM). The models represent the mentioned typical environments for railway applications. We compare the results with previous published intelligent transportation system (ITS-G5) measurement based models and highlight the differences

Measurement-Based Analysis on Vehicle-to-Vehicle Connectivity in Tunnel Environment

Vehicular ad hoc network (VANET) brings an excellent solution to ensure road safety and transportation efficiency in critical environment like tunnel. Particularly, radio link connectivity of vehicle-to-vehicle (V2V) significantly influences the performance of VANETs. The communication range of the radio systems is a random variable in reality due to the channel fading effect. Therefore, the connectivity model between vehicles in realistic environment is a key for accurate evaluation of system performances. In this paper, we study the V2V connectivity performance in the presence of channel randomness for tunnel environment. Firstly, based on channel measurement campaign, empirical path loss (PL) and small-scale fading channel models are established. Secondly, we study the influence of large-scale fading parameters on V2V connectivity. Thirdly, based on real small-scale fading characteristics, we derive the V2V connectivity probability between any two vehicles under Nakagami fading channel for one-dimensional VANET, and give the closed-form of V2V connectivity probability. Finally, we study the influences of various parameters (i.e., Nakagami fading factor, vehicle density, and neighbor order) on V2V connectivity performance. Results show that with the Nakagami fading shape factor increases, the connectivity probability increases. The shadowing fading can improve connectivity in the VANET; the path loss exponent, transmission distance, and signal-to-noise ratio (SNR) threshold have a negative impact on connectivity probability. The transmit power, vehicle density, and path loss threshold value have a positive impact on connectivity

Correlated Multimodal Imaging in Life Sciences:Expanding the Biomedical Horizon

International audienceThe frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that-in combination-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three mai