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Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel character-istics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.

As the backbone of the open science campaign, open science infrastructures (OSIs) play an increasingly important role in scientific innovation and the digital transformation of society. Nevertheless, OSIs still have a range of issues to contend with, particularly as they move towards more openness and a sustainable future. In this paper, as a case study of OSIs in China, we present the experience of operating CloudRT—the first and only open-access cloud-native high-performance computing ray-tracing platform in the world. Quantitative analysis demonstrates how such an OSI can produce an interdisciplinary impact beyond expectations. Moreover, the value of CloudRT is also shown through its acknowledgement by participants across the whole innovation chain, from pure science and engineering to applications in vertical industries. Both inspirations from this practice and its open challenges are discussed. Suggestions are put forward to guide the future efforts of stakeholders to develop a fruitful OSI ecosystem.

Terahertz (THz) communications are envisioned as a key technology for the sixth-generation wireless communication system (6G). However, it is not practical to perform large-scale channel measurements with high degrees of freedom at THz frequency band. This makes empirical or stochastic modeling approaches relying on measurements no longer stand. In order to break through the bottleneck of scarce full-dimensional channel sounding measurements, this paper presents a novel paradigm for THz channel modeling towards 6G. With the core of high-performance ray tracing (RT), the presented paradigm requires merely quite limited channel sounding to calibrate the geometry and material electromagnetic (EM) properties of the three-dimensional (3D) environment model in the target scenarios. Then, through extensive RT simulations, the parameters extracted from RT simulations can be fed into either ray-based novel stochastic channel models or cluster-based standard channel model families. Verified by RT simulations, these models can generate realistic channels that are valuable for the design and evaluation of THz systems. Representing two ends of 6G THz use cases from microscopy to macroscopy, case studies are made for close-proximity communications, wireless connections on a desktop, and smart rail mobility, respectively. Last but not least, new concerns on channel modeling resulting from distinguishing features of THz wave are discussed regarding propagation, antenna array, and device aspects, respectively.

In order to realize the vision of “smart rail mobility,” a seamless high-data rate wireless connectivity with up to dozens of gigahertz bandwidth will be required. This forms a strong motivation for exploring the underutilized millimeter wave (mmWave) and sub-mmWave bands. In this article, the wireless channel in one “smart rail mobility” scenario—the intrawagon scenario—is characterized through ultrawideband (UWB) channel sounding and ray tracing (RT) at mmWave and sub-mmWave bands. To begin with, a series of horizontal directional scan-sounding measurements are performed inside a real high-speed train wagon at 60 and 300 GHz frequency bands with a bandwidth of 8 GHz. Correspondingly, the channel characteristics such as Rician $K$ -factor, root-mean-square (rms) delay spread (DS), azimuth spread of arrival, and azimuth spread of departure are extracted and analyzed. Based on the measurements, a self-developed RT simulator is validated through the reconstruction of the three-dimensional wagon model and the calibration of the electromagnetic (EM) properties of the main materials. This gives the chance to physically interpret the measurement results and characterize the intrawagon mmWave and sub-mmWave channels more comprehensively through extensive RT simulations.

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.