To design and evaluate vehicle-to-vehicle (V2V) communication systems in intelligent transportation system (ITS), it is important to understand the propagation mechanisms and channel models of V2V channels. This paper aims to analyze the channel models at $5.2$ GHz for the highway environment in obstructed line-of-sight (OLoS) and line-of-sight (LoS) scenarios, particularly the vehicle connectivity probability derivation based on the propagation model obtained from measurement. First, the path loss (PL), shadow fading (SF), narrowband $K$-factor, and small-scale amplitude fading are analyzed. Results showed that the received signal magnitude follows Rice and Weibull distribution in LoS and OLoS scenarios, respectively. Second, we develop simple and low-complexity tapped delay line (TDL) models with a $10$ MHz bandwidth for LoS and OLoS scenarios; in addition, we investigate the wideband $K$-factor, the root mean square delay spread (RMS-DS), and delay-Doppler spectrum. Third, we derive the closed form connectivity probability between any two vehicles in the presence of Weibull fading channel, and analyze the effects of Weibull fading channel and traffic parameters on connectivity. It is found that Weibull fading parameter, transmit power and vehicle density have positive impact on connectivity probability, PL exponent has negative impact on connectivity probability.

Time-varying channel modeling plays an important role for many applications in time-variant scenarios, while most clustering algorithms focus on static channels and cannot accurately model the channel time-evolution properties. In this paper, a fuzzy clustering algorithm based on multipath component (MPC) trajectory is proposed. Firstly, both the distance and velocity similarities of the MPCs in different snapshots are taken into account to track the MPC trajectory, in which the fuzzy scheme and a kernel function are used to calculate the distance and velocity similarities, respectively. Secondly, a fuzzy MPC trajectory clustering algorithm is proposed to cluster the MPCs in multiple snapshots. The MPCs in a snapshot are clustered according to the membership, which is defined as the probability that a MPC belongs to different clusters. Finally, time-varying channels at 28 GHz are simulated to validate the performance of our proposed algorithm. The results show that our proposed algorithm is able to accurately identify the clusters in time-varying channels compared with the existing clustering algorithms.

Ultra-wideband (UWB) technology is a prospective technology for high-rate transmission and accurate localization in the future communication systems. State-of-art channel modeling approaches usually divide the UWB channel into several sub-band channels and model them independently. By considering frequency-dependent channel parameters, a novel analytical UWB channel model with continuous frequency response is proposed. The composite effect of all frequency components within the UWB channel on the channel impulse response (CIR) of delay domain is derived based on the continuous channel transfer function (CTF) of frequency domain. On this basis, a closed-form simulation model for UWB channels and geometry-based parameter calculation method are developed, which can guarantee the continuity of channel characteristics on the frequency domain and greatly reduce the simulation complexity. Finally, the proposed method is applied to generate UWB channel with 2 GHz bandwidth at sub-6GHz and millimeter wave (mmWave) bands, respectively. The channel measurements are also carried out to validate the proposed method. The simulated CIR and power gain are shown to be in good agreement with the measurement data. Moreover, the comparison results of power gain and Doppler power spectral density (DPSD) show that the proposed UWB channel model achieves a good balance between the simulation accuracy and efficiency.

In this paper, we propose a stochastic channel model in three-dimensional (3D) space for multiple input multiple output (MIMO) vehicle-to-vehicle (V2V) communications in dense urban environments. The movement of the mobile transmitter and mobile receiver results in the V2V channel model behave temporal non-stationarity. Therefore, the time-varying parameters of the propagation paths and angles are derived to characterize such property. Using this channel model, we investigate the propagation characteristics of V2V channels in terms of the road section and moving time/directions/speeds of the transmitter and receiver. Numerical results show that the theoretical results of the propagation characteristics of the V2V channel model are very close to those of the simulation ones, which show that the proposed channel model is suitable for depicting the V2V communications in dense urban scenarios.

This paper presented a novel millimeter-wave channel measurement platform for the 6G intelligent railway. This platform used phased array antenna with 64 elements and can support instant bandwidth up to 1 GHz. Combined with improved multi-tone sounding signals, the platform can enhance dynamic measurement capability in high-speed railway scenarios. We performed calibration works about frequency flatness, frequency offset and proved platform reliability with channel emulator based closed-loop verification. We also carried out field trials in high-speed railway carriage scenarios. Based on measurement results, we extracted channel characteristic parameters of path loss, power delay profile and delay spread to further verify the field measurement performance of the platform.

With the increasing demand for high bandwidth wireless communication systems, and with a congested spectrum in the sub-6 GHz frequency bands, researchers have been looking into exploration of millimeter wave (mmWave) and sub-terahertz (sub-THz) frequency bands. Channel modeling is essential for system design and performance evaluation of new wireless communication systems. Accurate channel modeling relies on reliable measured channel data, which is collected by high-fidelity channel sounders. Furthermore, it is of importance to understand to which extent channel parameters are frequency dependent in typical deployment scenario (including both indoor short-range and outdoor long-range scenarios). To achieve this purpose, this paper presents a state-of-art long-range 28 GHz and 300 GHz VNA-based channel sounder using optical cable solutions, which can support a measurement range up to 300 m and 600 m in principle, respectively. The design, development and validation of the long-range channel sounders at mmWave and sub-THz bands are reported, with a focus on their system principle, link budget, and back-to-back measurements. Furthermore, a measurement campaign in an indoor corridor is performed using the developed 300 GHz system and 28 GHz channel sounding systems. Both measured channels at the 28 GHz and 300 GHz channels are shown to be highly sparse and specular. A higher number of Multi Path Components (MPC) are observed for the 28 GHz system, while the same main MPC are observed for both systems.

As an important part of sixth generation (6G) integrated space-air-ground-sea networks, unmanned aerial vehicle (UAV) communications have aroused great attention and one of its typical application scenarios is the hilly environments. The related UAV air-ground (AG) channel characteristics analysis is crucial for system design and network evaluation of future UAV communications in hilly scenarios. In this paper, a recently conducted channel measurements campaign in a hilly scenario is presented, which is conducted at the center frequencies of 2.585 GHz and 3.5 GHz for different flight trajectories. Based on the measurement data, some key channel characteristics are analyzed, including path loss (PL), shadow fading (SF), Rician $\textit{K}$-factor, root mean square (RMS) delay spread (DS), and temporal auto-correlation function (ACF). Finally, the comparison of typical channel characteristics under circular and straight trajectories is given. The related results can provide a theoretical reference for constructing future UAV communication system in hilly scenarios.

Wireless channel characteristics have significant impacts on channel modeling, estimation, and communication performance. While the channel sparsity is an important characteristic of wireless channels. Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation, and improve system design and performance analysis. Compared with the traditional sub-6 GHz channel, millimeter wave (mmWave) channel has been considered to be more sparse in existing researches. However, most research only assume that the mmWave channel is sparse, without providing quantitative analysis and evaluation. Therefore, this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements. A vector network analyzer (VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz, and multi-scenario channel measurements are conducted. The Gini index, Rician $K$ factor and root-mean-square (RMS) delay spread are used to measure channel sparsity. Then, the key factors affecting mmWave channel sparsity are explored. It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel. In addition, the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.

Recently, whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel. This paper presents an environment information-based channel prediction (EICP) method for connecting the environment with the channel assisted by the graph neural networks (GNN). Firstly, the effective scatterers (ESs) producing paths and the primary scatterers (PSs) generating single propagation paths are detected by building the scatterer-centered communication environment graphs (SC-CEGs), which can simultaneously preserve the structure information and highlight the pending scatterer. The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers. Secondly, large-scale parameters (LSP) and small-scale parameters (SSP) are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs. Simulation results show that the average normalized mean squared error (NMSE) of LSP and SSP predictions are 0.12 and 0.008, which outperforms the methods of linear data learning.