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An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AI-based RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on high-performance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).

Firstly, the different characteristics of terahertz waves compared to lower frequency bands were summarized, including the influence of meteorological factors and rough surfaces of materials on THz waves.Next, it was proposed to use ray-tracing (RT) technology to calibrate the three-dimensional (3D) environment model and electromagnetic parameters of materials in the target scenarios through very limited channel measurement data.Then, the reversed parameters were utilized to perform in a similar but more general scenario, instead of a large amount of channel measurement data.Finally, the channel characteristics were analyzed by extraction of the channel key parameters, such as path loss, shadow fading, Rician K-factor, delay spread, angular spread, and doppler parameters in a mobile scenario.Representing two ends of 6G THz use cases from indoor scenario to outdoor scenario, case studies were made for wireless connections on a desktop and vehicular communications, respectively.As for the outdoor scenario, the influences of meteorological factors were additionally considered.The work is of great significance for the design and evaluation of the THz communication system.

Radio access technologies (RATs) are a key topic in railways, enabling them a better service rendering in terms of shorter headways between trains, higher safety levels and higher customer satisfaction. Very often, these railway RATs need a lot of time to be developed, tested and put into service, which implies a lack of efficiency and bottlenecks in the evolution of railway systems. To solve this situation, an emulation platform that considers both the physical layer and the network (this is, able to emulate the end-to-end chain) is envisaged in the EmulRadio4Rail project. Therefore, the physical layer of many railway scenarios must be emulated, which is a remarkable challenge because railways are very diverse. We see Tapped-Delay Lines (TDL) models as the most efficient way for emulation with the available hardware. In the literature, there are many TDL-based channel models for all the scenarios we considered but one: tunnels. Therefore, in order to fill this gap, we develop a novel TDL model for railway tunnels, considering the impact of the rolling stock (both high-speed railway (HSR) and subway trains). The proposed model allows the full characterization of this scenario in terms of power-delay-profile (PDP), Doppler spectrum and fading characteristics.

5G AgiLe and fLexible integration of SaTellite And cellulaR (5G‐ALLSTAR) is a Korea‐Europe (KR‐EU) collaborative project for developing multi‐connectivity (MC) technologies that integrate cellular and satellite networks to provide seamless, reliable, and ubiquitous broadband communication services and improve service continuity for 5G and beyond. The main scope of this project entails the prototype development of a millimeter‐wave 5G New Radio (NR)‐based cellular system, an investigation of the feasibility of an NR‐based satellite system and its integration with cellular systems, and a study of spectrum sharing and interference management techniques for MC. This article reviews recent research activities and presents preliminary results and a plan for the proof of concept (PoC) of three representative use cases (UCs) and one joint KR‐EU UC. The feasibility of each UC and superiority of the developed technologies will be validated with key performance indicators using corresponding PoC platforms. The final achievements of the project are expected to eventually contribute to the technical evolution of 5G, which will pave the road for next‐generation communications.

Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.

In recent years, the intelligent transport system (ITS) has been developed rapidly because of global urbanization and industrialization, which is considered as the key enabling technology to improve road safety, traffic efficiency, and driving experience. To achieve these goals, vehicles need to be equipped with a large number of sensors to enable the generation and exchange of high-rate data streams. Recently, millimeter-wave (mmWave) technology has been introduced as a means of meeting such a high data rate requirement. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link in mmWave band (22.1-23.1 GHz) for various road environments and deployment configurations is conducted. The self-developed ray-tracing (RT) simulator is employed with the calibrated electromagnetic (EM) parameters. The three-dimensional (3D) environment models are reconstructed from the OpenStreetMap (OSM). In the simulations, not only the vehicle user equipment (UE) moves, but also the other vehicles such as cars, delivery vans, and buses move around the vehicle UE. Moreover, the impacts of the receiver (Rx) multiple antennas and beam switching technologies at the vehicle UE are evaluated as well. The channel parameters of the V2I link in mmWave band, including received power, Rician $K$ -factor, root-mean-square delay spread, and angular spreads are explored in the target scenarios under different simulation deployments. This work aims to help the researchers understand the channel characteristics of the V2I links in mmWave band and support the link-level and system-level design for future vehicular communications.

With the challenge to form the networks of the Intelligent Transportation Systems (ITS), both technologies of vehicles and wireless communication are required to be connected tightly. In terms of wireless communication, the communication system in the terahertz (THz) frequency range with ultra-large bandwidth is a potential technology to support very high-data-rate wireless transmission at the age of beyond fifth-generation mobile communications (B5G). In this paper, the carrier frequency of 300 GHz with 8 GHz bandwidth vehicle-to-infrastructure (V2I) channel is characterized for the urban and highway scenario, respectively. The self-developed ray-tracing (RT) simulator is employed with the calibrated electromagnetic (EM) parameters. Since the wavelength of carrier frequency approaches the diameters of raindrops and snowflakes, the significant influence of the precipitation on the channel characterization is studied in our work as well. The large-scale parameters of the THz V2I channel, including path loss, Rician K-factor, root-mean-square (RMS) delay spread, and angular spreads are explored in the target scenarios under sunny, rainy, and snowy conditions, respectively. The channel characteristics studied in this paper can be used to support the link-level and system-level design for the future THz vehicular communications.

In the vision of intelligent transportation, vehicles are expected to feature with advanced applications, such as automatic road enforcement, dynamic traffic light sequence, and autonomous driving. Therefore, real-time and fast dynamic information exchanges are required, and vehicle-to-everything (V2X) communications are highly demanded. In this work, the channel characteristics of vehicular communication are analyzed in the millimeter-wave (mmWave) band at 22.1-23.1 GHz. Specifically, two types of links (the satellite link and the terrestrial link) are considered in urban and highway scenarios with different weather conditions. The ray-tracing simulator together with calibrated electromagnetic parameters is employed to practically generate wideband channels. The key channel parameters of each link including the received power, Rician K -factor, root-mean-square delay spread, and angular spreads are explored. The co-channel interferences between the two links are analyzed as well. The observations and conclusions of this work can be useful for the design of V2X communication technologies.

More people prefer using rail traffic for travel or for commuting owing to its convenience and flexibility. The railway scenario has become an important communication scenario in the fifth generation era. The communication system should be designed to support high‐data‐rate demands with seamless connectivity at a high mobility. In this paper, the channel characteristics are studied and modeled for the railway tunnel scenario with straight and curved route shapes. On the basis of measurements using the “Mobile Hotspot Network” system, a three‐dimensional ray tracer (RT) is calibrated and validated for the target scenarios. More channel characteristics are explored via RT simulations at 25.25 GHz with a 500‐MHz bandwidth. The key channel parameters are extracted, provided, and incorporated into a 3rd‐Generation‐Partnership‐Project‐like stochastic channel generator. The necessary channel information can be practically realized, which can support the link‐level and system‐level design of the communication system in similar scenarios.

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site.