Abstract-Measurement, Simulation, and Characterization of Train-to-Infrastructure Inside-Station Channel at the Terahertz Band

Ke Guan, Bile Peng, Danping He,  Johannes M. Eckhardt, Sebastian Rey, Bo Ai, Zhangdui Zhong

https://ieeexplore.ieee.org/document/8684885

In this paper, we measure, simulate, and characterize the train-to-infrastructure (T2I) inside-station channel at the terahertz (THz) band for the first time. To begin with, a series of channel measurements is performed in a train test center at 304.2 GHz with 8 GHz bandwidth. Rician K -factor and root-mean-square (RMS) delay spread are extracted from the measured power-delay profile. With the aid of an in-house-developed ray-tracing (RT) simulator, the multipath constitution is physically interpreted. This provides the first hand information of how the communicating train itself and the other train on site influence the channel. Using this measurement-validated RT simulator, we extend the measurement campaign to more realistic T2I inside-station channel through extensive simulations with various combinations of transmitter deployments and train conditions. Based on RT results, all cases of the target channel are characterized in terms of path loss, shadow fading, RMS delay spread, Rician K -factor, azimuth/elevation angular spread of arrival/departure, cross-polarization ratio, and their cross correlations. All parameters are fed into and verified by the 3GPP-like quasi-deterministic radio channel generator. This can provide the foundation for future work that aims to add the T2I inside-station scenario into the standard channel model families, and furthermore, provides a baseline for system design and evaluation of THz communications.

Abstract-Scenario modules, ray-tracing simulations and analysis of millimetre wave and terahertz channels for smart rail mobility

Ke Guan,  Bo Ai,  Bile Peng,   Danping He,  Xue Lin,  Longhe Wang, Zhangdui Zhong, Thomas Kürner

https://ieeexplore.ieee.org/document/8319730/

Millimeter wave (mmWave) and Terahertz (THz) technologies are the enabler of providing high-data rate communications for `smart rail mobility'. In this paper, six scenario modules for mmWave and THz train-to-infrastructure channels are defined and constructed. All the main objects, such as tracks, stations, crossing bridges, tunnels, cuttings, barriers, pylons, buildings, vegetation, traffic signs, billboards, trains, etc., are modeled according to the typical geometries and materials in reality. The three-dimensional (3D) models of these six modules are publicly available and freely downloadable. Furthermore, extensive ray-tracing simulations in the 60 GHz band with 8 GHz bandwidth are made in all the six modules with two antenna setups. Path loss exponent, shadow factor, Ricean K-factor, root-mean-square (RMS) delay spread, and coherence bandwidth are extracted and analyzed. These channel characteristics show that the objects that might not be so impacting on lower frequency channels indeed influence mmWave channel properties, and therefore, they can even play a more important role in the channels at higher frequency bands - THz. The channel characteristics analyzed in this study provide the foundation for future work that aims to streamline the design, simulation, and development of mmWave and THz communications enabling smart rail mobility.

Abstract-Three-Dimensional Angle of Arrival Estimation in Dynamic Indoor Terahertz Channels Using a Forward–Backward Algorithm

Bile Peng, Thomas Kürner,

http://ieeexplore.ieee.org/document/7539586/

A novel angle-of-arrival (AoA) estimation method for both azimuth and elevation based on Bayesian inference and statistical state transition probabilities is presented for the dynamic indoor terahertz (THz) channel. A precise AoA estimation is crucial for the deployment of a directive antenna, which can compensate for the high path loss and reduce the intersymbol interference. In many application scenarios, the user equipment is moved by the user during the data transmission, and the AoA is not constant. The novel algorithm exploits the fact that the AoA movement can be represented as a Markov process and that the Bayesian inference can be used to combine the likelihood and a priori information to provide a more precise estimate than using the likelihood alone. An indoor human movement model is developed to generate the realistic application scenario and obtain the statistical transition probabilities. The forward–backward algorithm is implemented to carry out the Bayesian inference. The algorithm performance is illustrated using the channel models generated by a ray launching simulator. The background log-likelihood is suggested to adapt the algorithm to the instant channel state change in a multipath environment.