Vehicle Wireless Communication Research Articles

An Error Protection Protocol for the Multicast Transmission of Data Samples in V2X Applications

There is a trend towards communication of larger data objects in wireless vehicle communication. In many cases, communication uses publish-subscribe protocols. Data rate requirements of such protocols are best addressed by wireless multicast protocols, but the existing protocols lack an error protection that is suitable for real-time and safety-critical applications. We present an application-aware protocol that supports the popular DDS (Data Distribution Service) middleware. By exploiting data object deadlines and slack for retransmissions and employing an adaptable, multicast-aware prioritization mechanism the reliable exchange of large data objects is enabled. The protocol is sufficiently general to be used on top of different communication standards such as 802.11- and cellular-based V2X (Vehicle-to-Everything) technologies. The protocol was implemented in an OMNeT++ simulation model and evaluated against recent state-of-the-art alternatives using parameters and constraints taken from a motivational truck platooning example. Furthermore, the protocol was implemented using an open-source DDS implementation as the basis and tested on a physical wireless demonstrator setup. The evaluation shows that the presented multicast protocol substantially outperforms the alternatives keeping streaming applications operational even under high frame error rates.

Files Cooperative Caching Strategy Based on Physical Layer Security for Air-to-Ground Integrated IoV

Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies.

The Importance of Ideological and Political Education in Vocational Engineering Majors and the Practice of the Course "Maintenance of Vehicle Wireless Communication Equipment"

The Importance of Ideological and Political Education in Vocational Engineering Majors and the Practice of the Course "Maintenance of Vehicle Wireless Communication Equipment"

Robustly String Stable Longitudinal Control for Vehicle Platoons Under Communication Failures: A Generalized Extended State Observer-Based Control Approach

This paper investigates the longitudinal control of vehicle platoons with a focus on external disturbances, parameter uncertainties, and communication failures. When vehicle platoons encounter vehicle to vehicle wireless communication failures, the preceding vehicle’s acceleration is unavailable, which degrades the performance of the system. However, the existing achievements can not be directly used to address the aforementioned three issues. To this end, a generalized extended state observer-based control (GESOBC) law is contrived. First, the parameter uncertainties and the external disturbances are together regarded as a lumped disturbance. Then, a generalized extended state observer is designed to estimate the lumped disturbance and the preceding vehicle’s acceleration, separately. Based on the estimation, a composite controller consisting of a state feedback control part and an estimation-based feedforward control part is developed. Furthermore, it is proved that the proposed GESOBC method can guarantee the exponentially bounded stability of the individual vehicle and the input to state string stability of the whole vehicle platoon. Finally, numerical simulations are conducted to demonstrate the effectiveness and feasibility of the proposed method.

Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy.

Research on Vehicle Collision Avoidance System through using Wireless Vehicle Communication

The number of vehicles in the world has been increasing for many years. With the serious aging of the world, the aging of drivers has become norm. Traffic safety is particularly important. The vehicle safety system has also been significantly improved, such as active braking, lane departure warning, 360-degree panoramic images, etc. All of which have greatly improved the safe driving of vehicle. On the other hand, Lack of the concentration of a driver and degeneration of the reaction time of driver operations are also the main causes of some traffic accidents. Especially, when a vehicle turns at a intersection, a driver must have high judgement. The purpose of this study is to develop the vehicle collision avoidance system through using wireless vehicle communication(VCAS). This system is constructed by a fixed point sensor set up by a road and vehicle information system. A fixed point sensor inform the traffic information to a vehicle. When the vehicle is dangerous condition, VCAS controls the vehicle and avoid a collision. In this paper we would like to examine the application in right turn collision avoidance at intersection.

Research on Distributed In-Vehicle Wireless Self-Organized Routing Protocol Distribution Mechanism

In recent years, the application of intelligent transportation systems has gradually made the transportation industry safe, efficient, and environmentally friendly and has led to a broader research prospect of vehicle wireless communication technology. Distributed vehicular self-organizing networks are mobile self-organizing networks in realistic traffic situations. Data interaction and transmission between nodes are achieved through the establishment of a vehicular self-organizing network. In this paper, a multipath routing protocol considering path stability and load balancing is proposed to address the shortcomings of existing distributed vehicular wireless self-organizing routing protocols. This protocol establishes three loop-free paths in the route discovery phase and uses the path stability parameter and load level parameter together to measure the total transmission cost. The one with the lowest total transmission cost is selected as the highest priority path for data transmission in the route selection phase, and the other two are used as alternate paths, and when the primary path breaks, the higher priority of the remaining path will continue to transmit data as the primary route. In this paper, to improve the content distribution performance of target vehicles in scenarios where communication blind zones exist between adjacent roadside units, an assisted download distribution mechanism for video-like large file content is designed in the V2V and V2I cooperative communication regime. That is, considering a two-way lane scenario, we use the same direction driving vehicles to build clusters, reverse driving vehicles to carry prefetched data, and build clusters to forward prefetched data to improve the data download volume of target vehicles in nonhot scenarios such as highways with the sparse deployment of roadside units, to meet the data volume download demand of in-vehicle users for large files and give guidance for efficient distribution of large file content in highway scenarios.

Vehicular Bidirectional Internal Antenna with Asymmetric Gain Characteristics to Compensate for Backward Link Path Loss due to Interior Obstacles

Wireless communication technologies are expected to become essential in future self-driving vehicles. This study presents an antenna for vehicle wireless communication with a bandwidth of 75 MHz at a Wireless Access in Vehicular Environment frequency of 5.885 GHz. To compensate for the backward link path loss from the vehicle interior and passengers, the antenna is designed to have asymmetric gains of 0 and 6 dBi at 0° and 180°, respectively. The antenna is validated through an outdoor road test. We measured the received continuous-wave (CW) power, received signal strength indicator, and packet-delivered ratio (PDR) of the digital signal under vehicle-to-vehicle communication mode. Similar power is received in both the backward and forward scenarios. The forward and backward PDR are also similar.

Features and differences between Desay SV and Ford in the solution of C-V2X Technology

C-V2X (means cellular vehicle to everything) is a vehicle wireless communication technology based on cellular network. In the early days of V2X, it was mainly based on DSRC. Its full name is dedicated short range communication, a dedicated short-range communication technology. C-V2X technology supports various vehicle networking applications including vehicle, vehicle road, vehicle person and vehicle network through device to device and cellular communication. C-V2X is a global standard formulated by 3GPP organization, including lte-V2X and 5g-V2X. Among them, LTE-V2X mainly carries basic traffic safety business. The standard has been formulated since 2015 and R14 version has been released in 2017; 5g-V2X, based on 5g NR technology, is mainly aimed at carrying automatic driving business, and will be released in 3GPP R16 at the end of 2019. This paper will compare the characteristics of C-V2X technology solutions of Ford and Desay SV, and explore the differences between Desay SV and Ford in C-V2X technology solutions [1].

Updating Point Cloud Layer of High Definition (HD) Map Based on Crowd-Sourcing of Multiple Vehicles Installed LiDAR

A high-definition (HD) map is becoming an integral component of future mobility systems such as autonomous and connected vehicles. Advances in computing systems, LiDAR technologies, and vehicle communication technologies have enabled the HD map to directly treat a point cloud map (PCM), modeling road environments as LiDAR signal-level data. However, if actual road environments are changed, the PCM, modeling the environments before changes, can not be used for vehicle applications. Accordingly, the PCM has to stay up-to-date states by reflecting the environment changes continuously. This paper presents a crowd-sourcing framework to update the PCM from environment changes continuously using LiDAR and vehicle communication. Multiple intelligent vehicles installed with the LiDAR sensors download the PCM from a map server via wireless vehicle communication. To minimize the effects of environment changes, a robust localization based on a hierarchical Simultaneous Localization and Mapping (SLAM) estimates the pose (position and direction). The estimated pose is used to detect the differences between the PCM and environments, which are defined as map changes. The map changes are detected by the probabilistic and evidential theory considering the LiDAR characteristics, such as beam divergence and multi-echo. The detected map changes are uploaded to the map cloud server and merged into the PCM. The proposed crowd-sourcing framework to keep the PCM up-to-date is verified and evaluated via simulations and experiments in sites with road environment changes.

V2VR: Reliable Hybrid-Network-Oriented V2V Data Transmission and Routing Considering RSUs and Connectivity Probability

Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

Physical Layer Security in UAV Systems: Challenges and Opportunities

Unmanned aerial vehicle (UAV) wireless communications has experienced an upsurge of interest in both military and civilian applications, due to its high mobility, low cost, on-demand deployment, and inherent line-of-sight air-to-ground channels. However, these benefits also make UAV wireless communication systems vulnerable to malicious eavesdropping attacks. In this article, we aim to examine the physical layer security issues in UAV systems. In particular, passive and active eavesdropping are two primary types of attack in UAV systems. We provide an overview on emerging techniques, such as trajectory design, resource allocation, and cooperative UAVs, to fight against both types of eavesdropping in UAV wireless communication systems. Moreover, the applications of non-orthogonal multiple access, multiple-input multiple-output, and millimeter-wave in UAV systems are also proposed to improve the system spectral efficiency and to guarantee security simultaneously. Finally, we discuss some potential research directions and challenges in terms of physical layer security in UAV systems.

Measurement-Based Modeling and Analysis of UAV Air-Ground Channels at 1 and 4 GHz

In the design of unmanned aerial vehicle (UAV) wireless communications, a better understanding of propagation characteristics and an accurate channel model are required. Measurements and comprehensive analysis for the UAV-based air-ground (AG) propagation channel in the vertical dimension are presented in this letter. Based on the measurement data at 1 and 4 GHz, the large-scale and small-scale channel parameters are extracted in the line-of-sight (LOS) and nonLOS case, respectively. The altitude-dependent path loss model is proposed herein. Furthermore, shadow fading and fast fading are statistically analyzed for comprehensively describing the fading behavior. Our results will be useful in the modeling of AG channels and the performance analysis for UAV-enabled wireless communication systems.

UAV-Enabled Data Collection: Multiple Access, Trajectory Optimization, and Energy Trade-Off

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

Safeguarding UAV Communications Against Full-Duplex Active Eavesdropper

Unmanned aerial vehicle (UAV) wireless communication has recently been recognized to be inevitable and prevalent in the fifth-generation (5G) wireless networks. In this paper, we propose a secure transmission scheme for a wiretap channel, where a source communicates with a legitimate UAV in the presence of an eavesdropper. We consider the full-duplex active eavesdropper, which performs both eavesdropping and malicious jamming simultaneously. The source transmits artificial noise (AN) signals, in addition to information signals, to confuse this eavesdropper. By considering the ground-to-UAV channel model, we analyze the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration. We further provide the asymptotic hybrid outage probability in a more compact form, where both the transmit power at the source and the jamming power at the eavesdropper become large with a fixed ratio. Through the analysis and the numerical results, we determine the optimal power allocation factor between information signals and AN signals as well as the operating height of UAV that minimize the hybrid outage probability. We also provide the most harmful antenna configuration of the eavesdropper to the UAV communications, and this paper can be a useful framework for the design of confidential UAV communication system.

Event-Based Adaptive Power Control in Vehicular Networked Systems With State-Dependent Bursty Fading Channels

This brief considers both safety and efficiency issues for vehicular networked systems (VNSs) where vehicles exchange information over vehicle to vehicle (V2V) wireless communication channels. One of the challenges to ensure system safety and efficiency lies in unreliable V2V fading channel conditions, which stochastically change as a function of the vehicle states and are subject to severe data rate drops. By adopting a bursty state-dependent fading model, this brief presents a novel event-based adaptive power control scheme. Under this scheme, the VNS assures efficient use of communication resources while preserving almost sure asymptotic stability. The benefits of the proposed scheme over other power policies are shown in a leader-follower formation control example.

Empirical Discussion of Reliable Wireless Communications in Vehicles

Empirical Discussion of Reliable Wireless Communications in Vehicles

Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment

Traffic congestion is a complex phenomenon triggered by a combination of multiple interacting factors. One of the main factors is the disturbances caused by individual vehicles, which cannot be identified in aggregate traffic data. Advances in vehicle wireless communications present new opportunities to measure traffic perturbations at the individual vehicle level. The key question is whether it is possible to find the relationship between these perturbations and shockwave formation and utilize this knowledge to improve the identification and prediction of congestion formation. Accordingly, this paper explores the use of three machine learning techniques, logistic regression, random forests, and neural networks, for short-term traffic congestion prediction using vehicle trajectories available through connected vehicles technology. Vehicle trajectories provided by the Next Generation SIMulation (NGSIM) program were utilized in this study. Two types of predictive models were developed in this study: (1) offline models which are calibrated based on historical data and are updated (re-trained) whenever significant changes occur in the system, such as changes/updates to the infrastructure, and (2) online models which are calibrated using historical data and updated regularly using real-time information on prevailing traffic conditions obtained through V2V/V2I communications. Results show that the accuracy of the models built in this study to predict the congested traffic state can reach 97%. The models presented can be used in various potential applications including improving road safety by warning drivers of upcoming traffic slowdowns and improving mobility through integration with traffic control systems.

Bi-Level Optimization Model for Greener Transportation by Vehicular Networks

In general, dynamic traffic signal control or smooth driving benefits energy saving and CO2 emissions. However, most of studies in the past lack the integrated optimization of traffic signals control and instantaneous vehicle motion states simultaneously on energy saving and CO2 emission reduction. In this paper, a bi-level optimization model is proposed to minimize fuel consumption and CO2 emissions by considering real-time traffic signal control in road side unit (RSU) and instantaneous vehicle motion states optimization in on-board unit (OBU) synthetically. The RSU communicates with OBUs by vehicle wireless communication networks. Then, the traffic signal scheme in RSU is optimized with the received vehicle motion data sent from OBUs, and the system in vehicles optimize their speed and acceleration with the received traffic signal scheme sent from RSU. The simulation results indicate that the proposed model outperforms the existing Maximize Throughput Model (MaxTM) up to 10% in reducing fuel consumption and CO2 emissions especially when the traffic is heavy.

How connectivity is transforming the automotive ecosystem

This paper brings together both a technical and a business approach about the introduction of wireless communications in vehicles and how this is affecting the automotive sector. It also provides complementary insights on how business opportunities can affect the selection of different available wireless technologies. Different perspectives on how the automotive sector is being transformed are also described.