Vehicle-to-vehicle (V2V) communications have received a lot of attention as it can significantly improve efficiency and safety of road traffic. In general, V2V scenarios have some special features such as high mobility of transceivers, low antenna height, and small communication ranges, which significantly affect the propagation characteristics. Therefore, an accurate channel model is required to characterize V2V propagation channel. However, some important features of V2V channels have not been well characterized. For example, most existing V2V channel models tend to only consider the distribution of multipath components (MPCs) in horizontal dimension, whereas ignoring vertical dimension, which is inconsistent with the distribution of MPCs in the actual channel. Moreover, the dynamic clusters of MPCs have not been well modeled in the existing V2V models. Therefore, in order to better model the V2V channel, a cluster-based three-dimensional (3D) channel model is proposed in this paper, which is based on the measurements conducted at 5.9 GHz in urban and suburban scenarios. In the proposed model, the distribution of MPCs clusters in both horizontal and vertical dimensions is considered. The Space-Alternating Generalized Expectation-maximization (SAGE) algorithm is used to extract MPCs, and the clustering and tracking algorithms are used to identify and track the clusters of dynamic MPCs. In the proposed model, all MPC clusters are divided into two categories: global-clusters and scatterer-clusters, and the distribution of the two clusters are characterized by a series of inter and intra-cluster parameters. It is found that both the azimuth spread and the elevation spread follow the lognormal distribution. In addition, the power of MPCs within a cluster has the truncated-Gaussian distribution, whereas the angle of MPCs within a cluster has the Laplacian distribution. Finally, the accuracy of the model is verified by comparing the measurements and simulations.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Vehicular communication has been widely concerned by academia and industry. Wireless channel characterization and modeling are the foundation of communication systems. Compared with typical road scenarios such as urban and suburban areas, wireless channel characterization in intersections is a challenging task. In this paper, channel measurements at 5.9 GHz in street intersection scenarios are carried out and provide data for the characterization and modeling of time-varying vehicular channels. This paper proposes a double-slope path loss model, which can accurately reflect the signal propagation loss at intersections. Further, the time-varying spatial characteristics of multipath are extracted and analyzed, and the azimuth and elevation spread of arrival are statistically characterized. The influence of obstacles on multipath spatial characteristics in intersection scenarios is investigated. Then we investigate the spatial stationarity of channels and analyze the possible reasons for the differences in spatial stationarity of different scenarios. In addition, based on multipath extraction, we quantitatively analyze the energy of dense multipath components in different propagation scenarios. The research in this paper can enrich the investigation of vehicular channels and enable the analysis and design of vehicular communication systems.

Obtaining angle-of-arrival (AOA) information is of great significance to improve the performance of communication systems. Real-time AOA recognition can reduce the complexity of beamforming design for massive multiple-input multiple-output (MIMO) systems, and can be used to construct an outer precoder to optimize the system and rate. However, for vehicular communications, AOA will change with environment and positions of vehicles, and it is difficult to obtain accurate AOA in real-time. Therefore, a fast AOA recognition method is needed to adapt to the rapid changes of channels. The traditional spectral- or parametric-based AOA estimation methods are difficult to obtain real-time AOA information because of the relatively high computational complexity. In order to solve this problem, this paper proposes a machine-learning-based fast AOA recognition approach. The proposed method includes off-line training and on-line estimation processes. In the off-line training process, an estimation model is obtained by using the support vector machine (SVM) based on a large number of actual measurement data in vehicular scenarios. Then, in the on-line estimation process, the obtained model is used to realize fast AOA recognition according to the channel snapshots collected by antenna array. Furthermore, the performance is verified under the different conditions of SVM parameters, training features, antenna numbers, and training data sizes. The experimental results show that the proposed method has satisfactory accuracy in real-time AOA recognition, and the optimal configuration and implementation scheme are also discussed.