Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.

A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

The dynamic environment due to traffic mobility and wireless communication from/to vehicles make identity authentication and trust management for privacy preservation based on vehicular edge computing (VEC) an increasingly important problem in vehicular networks. However, existing authentication schemes mainly focus on communication between a single trusted edge computing node and multiple vehicles. This framework may suffer the bottleneck problem due to the single edge computing node, and the performance depends heavily on its resources. In this paper, a blockchain empowered group-authentication scheme is proposed for vehicles with decentralized identification based on secret sharing and dynamic proxy mechanism. Sub-authentication results are aggregated for trust management based blockchain to implement collaborative authentication. The edge computing node with a higher-reputation stored in the tamper-proof blockchain can upload the final aggregated authentication result to the central server to achieve the decentralized authentication. This work analyzes typical attacks for this scheme and shows that the proposed scheme achieves cooperative privacy preservation for vehicles while also reducing communication overhead and computation cost.

Blockchain Empowered Cooperative Authentication With Data Traceability in Vehicular Edge Computing

Sparse channel arises in a number of applications in wireless communications such as channel estimation and signal processing. There is growing evidence that physical wireless channels exhibit a sparse structure, and channel sparsity has been even considered as a nature of channels in many recent research works. However, there still lacks a good measure of channel sparsity, and mostly the assumptions that channel is sparse or non-sparse are based on intuitive analysis without measurement validation, which leads to some contradictions. In this article, based on channel measurement data, it is pointed out that a widely-used assumption, that wireless channels can be considered to be sparse, has pitfalls. Without loss of generality, the measurements are conducted in an urban scenario with different degrees of channel multipath richness. The channel degrees of freedom, diversity measure, and the Ricean K factor are used to evaluate channel sparsity, and they are found to have fairly high accuracy of measuring degrees of channel sparsity. It is also observed from measurements that the degree of channel sparsity is not steady and a sparse channel may change to non-sparse within a short time/distance observation window. Moreover, sparse and non-sparse based channel estimators are evaluated based on the measurements and the performances are analyzed. The results show that a sparse channel estimator cannot guarantee stable estimation accuracy even in channels with a high degree of sparsity, and considerable performance degradation will occur if a channel changes to non-sparse, which actually often happens in realistic communication scenarios and should be carefully considered in performance analysis. Some sparse channel related technical issues are also discussed in the article.

Wireless Channel Sparsity: Measurement, Analysis, and Exploitation in Estimation

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 a 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 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.

A Cluster-Based Three-Dimensional Channel Model for Vehicle-to-Vehicle Communications

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

Measurements and Cluster-Based Modeling of Vehicle-to-Vehicle Channels With Large Vehicle Obstructions

Scenario identification plays an important role in improving communication system performance Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications

Machine-Learning-Based Scenario Identification Using Channel Characteristics in Intelligent Vehicular Communications

In this paper, we focus on the analysis and modeling of path loss characteristics for vehicle-to-vehicle (V2V) communications with vehicle obstructions. A series of channel measurements were performed at 5.9 GHz in a typical urban scenario for V2V communications. In these measurements we subdivided into three types of test cases: non-obstruction, smallvehicle obstruction, and large-vehicle obstruction. Then the path loss and shadow fading components of the three cases are extracted, compared and analyzed. Based on the measurements, we show the influence of different types of vehicle obstruction on path loss and shadow fading characteristics. It is found that small-vehicle obstruction does not significantly affect the mean of path loss, and it leads to additional shadow fading of 3 dB. Large vehicle obstruction brings about 10 dB of additional path loss. Finally, a path loss model is proposed, which includes the influence of vehicle occlusion on path loss and employs the classical log-distance path loss formula. The results in the paper could be used for the performance analysis and system design of V2V communication.

Path Loss Analysis and Modeling for Vehicle-to-Vehicle Communications with Vehicle Obstructions

As an important part of the intelligent transportation system (ITS), vehicle-to-vehicle (V2V) communication will improve the safety and efficiency of the transportation system by realizing the information transmission between vehicles. The underground parking garage is a typical scenario in V2V communication, but as an indoor environment, it is different from the conventional outdoor road scenarios significantly. Therefore, the purpose of this paper is to analyze and model the V2V channel characteristics of an underground garage. In this paper, a novel channel measurement carried out in the underground garage is first introduced. Considering that there are a lot of obstacles in an underground garage, the non-line-of-sight (NLoS) condition is taken into account during the measurement. Then, channel characteristic parameters, including large-scale fading, delay spread, and K-factor, are analyzed based on the measured data. In addition, we also carry out channel angle of arrival analysis, show the distribution of azimuth of arrival (AOA) and elevation of arrival (EOA) of multipath components (MPC), and conduct a statistical analysis of the angular spread. In the process of analysis and modeling, we focus on the differences between line-of-sight (LoS) and NLoS conditions. The analysis and conclusions presented in this paper will enrich the understanding of V2V channel and benefit the design of V2V communications.

Liang Chen

Papers

A Cluster-Based Three-Dimensional Channel Model for Vehicle-to-Vehicle Communications

Measurements and Cluster-Based Modeling of Vehicle-to-Vehicle Channels With Large Vehicle Obstructions

Machine-Learning-Based Scenario Identification Using Channel Characteristics in Intelligent Vehicular Communications

Path Loss Analysis and Modeling for Vehicle-to-Vehicle Communications with Vehicle Obstructions

V2V channel characterization and modeling for underground parking garages