Fang Li

As one of the most important radio communication scenarios in vertical industry, the high‐speed railway (HSR) station is facing the challenge of coverage optimization due to its complex structure. Regarding the wireless network planning and optimization of HSR stations as a part of the customized network, this paper makes an analysis on the 5G‐R channel in the HSR station scenario. Channel characteristics, including path loss, power ratio (PR), and angular spread (AS), are extracted on the basis of ray tracing (RT). Multipath components can also be distinguished based on RT. In order to achieve a better performance, the reconfigurable intelligent surface (RIS) technology is adopted to the network deployment. Moreover, machine learning (ML) is used to locate the best direction of the beam. The analysis results show that the received power in RIS‐assisted channels is significantly promoted. Our research can provide a deep understanding to the 5G‐R channel in station scenario and a well reference for the design and optimization of the customized network.

In this work, we study the problem of Doppler shift and channel estimation for wireless communication systems on high-speed railways (HSRs). We focus on tunnel scenario, one of the classical scenarios of HSRs. We first build up the mathematical system model, design a joint Doppler shift and channel estimator, and compare its performance with the typical Moose algorithm. We show that our estimator outperforms the Moose algorithm in Doppler estimation. Besides, since wireless channels in tunnel scenarios often contain several or multiple taps, we suggest an adaptive frame structure to improve transmission efficiency. Simulations are then provided to corroborate our proposed studies.

Vehicular ad-hoc network (VANET) is one of the most important components to realizing intelligent connected vehicles, which is a high-commercial-value vertical application of the fifth-generation (5G) mobile communication system and beyond communications. VANET requires both ultrareliable low latency and high-data rate communications. In order to evolve towards the reconfigurable wireless networks (RWNs), the 5G mobile communication system is expected to adapt the key parameters of its radio nodes rapidly. However, the current propagation prediction approaches are difficult to balance accuracy and efficiency, which makes the current network unable to perform autonomous optimization agilely. In order to break through this bottleneck, an accurate and efficient propagation prediction and optimization method empowered by artificial intelligence (AI) is proposed in this paper. Initially, a path loss model based on a multilayer perception neural network is established at 2.6GHz for three base stations in an urban environment. Not like empirical models using environment types or deterministic models employing three-dimensional environment models, this AI-empowered model explores the environment feature by introducing interference clutters. This critical innovation makes the proposed model so accurate as ray tracing but much more efficient. Then, this validated model is utilized to realize a coverage prediction for 20 base stations only within 1 minute. Afterward, key parameters of these base stations, such as transmission power, elevation, and azimuth angles of antennas, are optimized using simulated annealing. This whole methodology paves the way for evolving the current 5G network to RWNs.

The vehicle-to-vehicle (V2V) radio channel is non-stationary due to the rapid movement of vehicles. However, the stationarity of the V2V channels is an important indicator of the V2V channel characteristics. Therefore, we analyzed the non-stationarity of V2V radio channels using the local region of stationarity (LRS). We selected seven scenarios, including three directions of travel, i.e., in the same, vertical, and opposite directions, and different speeds and environments in a similar driving direction. The power delay profile (PDP) and LRS were estimated from the measured channel impulse responses. The results show that the most important influences on the stationary times are the direction and the speed of the vehicles. The average stationary times for driving in the same direction range from 0.3207 to 1.9419 s, the average stationary times for driving in the vertical direction are 0.0359–0.1348 s, and those for driving in the opposite direction are 0.0041–0.0103 s. These results are meaningful for the analysis of the statistical characteristics of the V2V channel, such as the delay spread and Doppler spread. Small-scale fading based on the stationary times affects the quality of signals transmitted in the V2V channel, including the information transmission rate and the information error code rate.

In this study, radio channel measurements were conducted in an urban inland river environment at 5.9 GHz. The measurements consisted of both the line of sight (LOS) and non-line of sight (NLOS) cases. We estimate and model channel characteristics based on the measured data. For small-scale fading properties, best fit small-scale fading distribution is determined. Statistical models of the Ricean K-factors and Weibull shape parameters are provided. In addition, a distance-dependent Ricean K-factor model for the urban inland river environment is proposed. For the power delay profile (PDP), we perform cluster identification and obtain the cumulative distribution function (CDF) of the inter-cluster interval. A statistical model of the intracluster decay time constant is also presented, and an exponential decay model is applied for modeling the inter-cluster decay of the PDP. For delay spread and Doppler spread, the root mean square (RMS) delay spreads in the LOS case are compared with those in the NLOS case. Moreover, we estimate and analyze RMS Doppler spreads in both the LOS and NLOS cases. Finally, a bimodal Gaussian mixture distribution (BGMD) is employed to characterize the RMS delay spreads and RMS Doppler spreads. The BGMDs exhibit good matching levels.

Vehicular ad hoc networks (VANETs) are an important part of the next generation of intelligent transportation. For multimedia information transmissions, VANETs require high speed and a low-delay link between the vehicles. This paper focuses on a connectivity analysis of VANET high-speed and low-delay connections under different traffic densities and adverse weather conditions. In this paper, a channel measurement at 5.9 GHz under two kinds of traffic densities is first carried out. Then, the measured data for the path loss are used in the VANET model for the connectivity probability. Finally, we consider the influence of adverse weather conditions on the wireless signal and simulate the influence on the high-speed and low-delay data connectivity of VANETs. The results show that adverse weather will have a certain impact on vehicle-to-vehicle (V2V) communication.

This paper reports the results of a car-following measurement of the wireless propagation channel at 5.9 GHz on a seriously congested urban road in Wuhan, China. The small-scale amplitude-fading distribution was determined to be a Ricean distribution using the Akaike information criterion. This result shows that this car-following scenario can be regarded as a line-of-sight radio channel. Moreover, the statistical K-factor features follow a Gaussian distribution. According to the power delay profile and average power delay profile, we found that street buildings in this dense urban environment contributed to very strong reflection phenomena. The impact of a powerful reflection is analyzed through path loss, delay, and Doppler spreads in the channel statistical properties. In the frequency domain, we observe a U-shape delay-Doppler spectrum that proved that the dense urban scenario consists of scattering channels. All these results are summarized in tabular form that will be useful in the modeling of vehicle-to-vehicle wireless communication systems.