Vehicular Communication Research Articles

Vehicular visible light communications noise analysis and modeling.

Vehicular visible light communications (VVLC) is promising intelligent transportation systems technology with the utilization of light-emitting diodes. The main degrading factor for the performance of VVLC systems is noise. Traditional VVLC systems noise modeling is based on the additive white Gaussian noise assumption in the form of shot and thermal noise. In this paper, to investigate both time correlated and white noise components of the VVLC channel noise, we propose a noise analysis based on Allan variance, which provides a time-series analysis method to identify noise from the data. The results show that white noise and random walk are observed in the VVLC systems. We also propose a motion detection algorithm based on the adaptive Gaussian mixture (GM) model to generate a double Gaussian model of VVLC channel noise. We further present a study on the error performance of a VVLC system considering channel noise to be a mixture of Gaussian components. We derive the analytical expressions of probability of error for binary phase-shift keying and quadrature phase-shift keying constellations. It has been observed that, in the presence of GM noise, the system performance degrades significantly from the usual one expected in a Gaussian noise environment and becomes a function of the mixing coefficients of the GM distribution.

On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications

Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements.

Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities

The development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate.

An intelligent computational approach of signal control in urban rail transit for vehicular communication

An intelligent computational approach of signal control in urban rail transit for vehicular communication

Performance analysis of vehicle-to-everything communication using internet of LoRa computing for intelligent transportation system

Intelligent Transportation System (ITS) is an emerging field nowadays that is widely utilized to improve safety measures, avoid abnormalities, and traffic flow control, and also develops the environment without hassle. So far, the deployment of sensors into vehicles and the analyzing the vehicular parameters towards the smart city applications have been achieved by the integration of LoRa-based vehicular communication. However, trust in previous design architecture should need efficient transmission, robustness, and energy efficiency. To overcome the challenges, the proposed system designed the Internet of LoRa computing enabled vehicular communication with high reliability by offering the optimization technique namely an Enhanced Artificial Bee Colony (EABC) algorithm for the localization scheme. The proposed framework consists of two sections. First, observe the objects nearby vehicles using an ultrasonic sensor that is equipped in the Arduino module with a LoRa shield. The second work contributes to the evaluation of performance metrics of vehicular communication in the sensing region with a minimum delay of two seconds using MathWorks simulation. The article designed the VANET, which utilized the LoRa architecture for Vehicle to Everything communication, and pointed out the position of the sensor nodes using a localization scheme (EABC algorithm), comparing the proposed EABC algorithm with the other optimization techniques viz Particle Swarm Optimization and Genetic algorithms in the dense nodes and it achieves 25% variation in minimizing the position error at a certain speed. Further, find the system performance by calculating the BER (Bit Error Rate) in both coherent and non-coherent with varying speeds of the vehicle and router connections and it achieves 40% variation in efficiency and realizes the network coverage in terms of the position of the vehicle in the way the proposed framework achieves the high accuracy in overall system throughput.

VERCO: A privacy and data-centric architecture for verifiable cooperative maneuvers

Cooperative, Connected and Automated Mobility (CCAM) applications, enabled by vehicular communications and vehicle automation technologies, are set to increase traffic safety and efficiency. An important feature of CCAM is the potential to decide and coordinate maneuvers among automated vehicles in a more efficient and secure manner, when compared to human drivers. However, maneuver decision and coordination is a rather complex topic due to the multitude of possible maneuvers and the occasional cooperation failures, as caused by issues derived from the use of wireless communications in the vehicular ecosystem, like packet loss or even misbehaving entities. This work focuses on the latter problem by introducing a secure communication design for the decision of general cooperative maneuvers and the distributed storage of related data for accountability purposes using mechanisms based on distributed ledger technologies, while meeting user and data privacy requirements. We present the VERCO (VERifiable COoperation), a scalable geographical-based multi-layered blockchain architecture able to support the high volume of shared vehicular data in order to enhance the security and accountability of cooperative maneuvers, as well as potentially being able to support other vehicular services. To support this architecture, we also provide a performant communication protocol for the decision and negotiation of cooperative maneuvers, based on a new message: the Verifiable Cooperation Message (VCM). The architecture and communication protocol are analyzed and tested employing hardware-in-the-loop (HiL) simulations using two ETSI ITS standard compliant on-board units (OBUs) while deciding maneuvers in a lane-merging scenario. To test the scalability of the architecture a roadside unit (RSU) is put under different stress loads using a variable number of simulated vehicles in the same lane-merging scenario. Experimental results show the feasibility of the communication protocol, with an average delay of 34.68 ms, and the lightweightness of the proposed architecture, with an average overhead of only 2.62 ms for two vehicles and less than 6 ms for dozens of vehicles.

A review on machine learning based routing protocols for delay tolerant networks

Nowadays, Delay Tolerant Network plays an important role in improving the communication between the network nodes. Applications of Delay Tolerant Network are disaster recovery, vehicular communication, sensor networks, interplanetary networks, and communication in remote and rural areas. Routing is one of the important tasks for enhancing the energy effectiveness of data transmission among the mobile nodes under network congestion and dynamic topology. Machine Learning-based routing algorithms are used for improving network communication in Delay Tolerant Networks. Its objective is to reduce the delay, minimize the overhead, reduce energy consumption, improve throughput, minimize packet loss, and efficient data transmission. This paper presents a comprehensive review of routing algorithms using machine learning for Delay Tolerant Networks.

Ultra wideband (UWB) dielectric resonator antenna using fractal‐inspired feeding mechanism

SummaryThis article examines a compact dielectric resonator antenna (DRA) for UWB applications. Here a composite feeding structure excites a dielectric resonator which in tern provides the resonant modes TE111, TE121, TE212, and TE222. The dielectric resonator (DR), built of alumina ceramic (εr, DR = 9.8), is mounted on a fractal triangular patch, inspired by Sierpinski Gasket. The suggested DRA is supported by a FR4 substrate (εr, sub = 4.4) and measures compactly 40 × 30 × 8.5 mm3. To confirm the results of its simulations, the proposed antenna's prototype is prepared and measured up to the second iteration. The measured outcome demonstrates that the suggested antenna has a frequency range of 3.38–10.71 GHz (104%) for S11 < −10 dB and provides a maximum gain of 7.23 dBi at 8 GHz along with highest possible simulated efficiency of 98%. The suggested DRA is appropriate for small‐range future 4G/5G UWB wireless multimedia applications due to its ultrawide bandwidth, excellent gain, reasonable efficiency, omnidirectional radiation features, and compact construction. This DRA is also suitable for repeaters of mobile and vehicular communications.

5G-MEC Testbeds for V2X Applications

Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network’s edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds.

Stereoscopic UWB Yagi-Uda Antenna with Stable Gain by Metamaterial for Vehicular 5G Communication.

In this paper, a stereoscopic ultra-wideband (UWB) Yagi-Uda (SUY) antenna with stable gain by near-zero-index metamaterial (NZIM) has been proposed for vehicular 5G communication. The proposed antenna consists of magneto-electric (ME) dipole structure and coaxial feed patch antenna. The combination of patch antenna and ME structure allows the proposed antenna can work as a Yagi-Uda antenna, which enhances its gain and bandwidth. NZIM removes a pair of C-notches on the surface of the ME structure to make it absorb energy, which results in two radiation nulls on both sides of the gain passband. At the same time, the bandwidth can be enhanced effectively. In order to further improve the stable gain, impedance matching is achieved by removing the patch diagonally; thus, it is able to tune the antenna gain of the suppression boundary and open the possibility to reach the most important characteristic: a very stable gain in a wide frequency range. The SUY antenna is fabricated and measured, which has a measured -10 dBi impedance bandwidth of approximately 40% (3.5-5.5 GHz). Within it, the peak gain of the antenna reaches 8.5 dBi, and the flat in-band gain has a ripple lower than 0.5 dBi.

Dynamic V2V Channel Measurement and Modeling at Street Intersection Scenarios

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.

Heterogeneous Architecture for DSRC/LTE Vehicular Communication Networks Based on the ITS Reference Architecture with Fuzzy Logic for Decision-Making

Methods to integrate different access technologies such as heterogeneous communication architecture remains a challenge. Among a variety of V2X-supporting access technologies, Dedicated Short Range Communication (DSRC) and cellular communications, e.g., Long Term Evolution (LTE) is promising reliable and efficient vehicular communications. DSRC has been designed to allow for direct low-latency communications among different vehicles (i.e., V2V) and between vehicles and roadside units (RSU). On the other hand, DSRC suffers from link quality degradation with the presence of buildings and vehicles, especially in urban areas, where channel collisions become serious when vehicle density is high. LTE networks can provide wide area coverage and are favorable to bandwidth-greedy applications, which require high data rates and reliability. Considering the relatively high end-to-end delay for message transmission due to the long transmission time interval (TTI) current LTE networks have drawbacks regarding latency to support highfrequency safety-related information exchange among vehicles in local areas. Combining LTE and DSRC approaches as a heterogeneous solution is essential to fast introduce V2X services for future automated driving. By this intelligent Vertical Handover (VHO) algorithms are needed to ensure seamless connectivity of vehicles to the best network at a particular point in time. For this paper, we propose a 3- input fuzzy-logic-based VHO scheme for Heterogeneous DSRC/LTE Vehicular Communication Networks. The simulation results in this paper show that the proposed 3- input fuzzy-logic model can be able to make decisions and select the best network to connect to base on the network with the highest HF value. The 3-input fuzzy-logic model was also tested in a vehicular highway scenario simulation. The proposed model shows optimized communication results in terms of the mean end-to-end delay against that of the literature.

Efficient Detection and Localization of DoS Attacks in Heterogeneous Vehicular Networks

Vehicular communication has emerged as a powerful tool for providing a safe and comfortable driving experience for users. Long Term Evolution (LTE) supports and enhances the quality of vehicular communication due to its properties such as high data rate, spatial reuse, and low delay. However, high mobility of vehicles introduces a wide variety of security threats, including Denial-of-Service (DoS) attacks. In this paper, we propose effective solutions for real-time detection and localization of DoS attacks in an LTE-based vehicular network with mobile network components (e.g., vehicles, femto access points, etc.). We consider malicious data transmission by vehicles in two ways- using real identification (unintentional) and using fake identification (intentional). This paper makes three important contributions. First, we propose an efficient attack detection technique based on data packet counter and average Packet Delivery Ratio (PDR). Next, we present an improved attack detection framework using machine learning algorithms. We use some ML-based supervised classification algorithms to make detection more robust and consistent. Finally, we propose Data Packet Counter (DPC)-based, triangulation-based and measurement report based localization for both intentional and unintentional DoS attacks. We analyze the average packet delay incurred by vehicles by modelling the system as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M/M/m$</tex-math></inline-formula> queue. Our experimental evaluation demonstrates that our proposed technique significantly outperforms state-of-the-art techniques.

Physical layer security analysis using radio frequency‐fingerprinting in cellular‐V2X for 6G communication

AbstractIt is anticipated that sixth‐generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context‐aware security protocol. Physical layer security solutions can compete for low‐complexity, low‐delay, low‐footprint, adaptable, extensible, and context‐aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF‐FP) for location estimation is proposed, wherein the RF‐FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road‐side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF‐FP scheme increases SC by up to 25% for the same signal‐to‐noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF‐FP‐based location estimation provides much better results as compared to the existing physical layer security schemes.

UAV-Aided Vehicular Short-Packet Communication and Edge Computing System Under Time-Varying Channel

In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.

A Compact Low-Profile Differential Dual-Polarized Filtenna Without an External Filter Circuit for Vehicular Communications

A compact low-profile differential dual-polarized filtenna without an external filter circuit for vehicular communications is proposed in this paper. It has a compact, low-profile, simple, and symmetrical structure, which consists of four identical quarter circular patches with a pair of crossed feeding lines symmetrically and orthogonally embedded. Good filtering performance with three radiation nulls (RNs) generated without extra filtering structure is achieved, and the production mechanisms of three RNs are studied numerically. To get a deep insight into its working mechanism, the proposed structure is analyzed, and the equivalent lumped circuit is modeled and simulated. The prototype antenna with low profile of 2.832 mm and average gain of about 9.1 dBi within the frequency band of 5.28-6.11 GHz is fabricated and measured. Moreover, the measured polarization isolation is more than 44 dB within the operating band and the measured cross-polarization level is less than -29 dB. Good agreement between simulated and measured results can be acquired. The proposed antenna is a good candidate for in-vehicle local area networks (WLAN), in-vehicle Wi-Fi 6E, vehicle-to-vehicle (V2V) communication and 5G vehicle-to-basestation communication applications.

A Novel Mobility Induced Channel Prediction Mechanism for Vehicular Communications

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.

Framework for Simulation of Vehicular Communication using LSTM-based Graph Attention Networks

Framework for Simulation of Vehicular Communication using LSTM-based Graph Attention Networks

Modern 5G MIMO Antenna for Vehicular Communication and Safety

A Modern 5G Multiple Input Multiple Output (MIMO) antenna alignment with frequency range for the 5G NR-n2 band (1.81 GHz) and protection band (5.85 GHz) that are dual-band for automobile communication and safety is offered. In a relatively shrunken dimension in relation to its working wavelength, the smallest frequency of resonance is relatively challenging to attain. The antenna's dimensions are 60 mm × 60 mm × 1.6 mm and it achieves a gain of 4.65 dBi and 3.38 dBi, at the frequency range of 1.81 GHz and 5.85 GHz. Because of the presence of ground slot, MIMO antennas offer isolation values of more than 19 dB and diversity gains that are comparatively close to 10 dB. In GSM and safety applications, this enhanced square single-element and MIMO antenna offers a noticeably increased gain and improved performance. The on-vehicle study of MIMO setups is performed to ensure the durability of the specified antenna in an automotive application.

RIS-Enabled Secret Key Generation for Secured Vehicular Communication in the Presence of Denial-of-Service Attacks.

Wireless communication has become an integral part of modern vehicles. However, securing the information exchanged between interconnected terminals poses a significant challenge. Effective security solutions should be computationally inexpensive, ultra-reliable, and capable of operating in any wireless propagation environment. Physical layer secret key generation has emerged as a promising technique, which leverages the inherent randomness of wireless-channel responses in amplitude and phase to generate high-entropy symmetric shared keys. The sensitivity of the channel-phase responses to the distance between network terminals makes this technique a viable solution for secure vehicular communication, given the dynamic behavior of these terminals. However, the practical implementation of this technique in vehicular communication is hindered by fluctuations in the communication link between line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. This study introduces a key-generation approach that uses a reconfigurable intelligent surface (RIS) to secure message exchange in vehicular communication. The RIS improves the performance of key extraction in scenarios with low signal-to-noise ratios (SNRs) and NLoS conditions. Additionally, it enhances the network's security against denial-of-service (DoS) attacks. In this context, we propose an efficient RIS configuration optimization technique that reinforces the signals received from legitimate users and weakens the signals from potential adversaries. The effectiveness of the proposed scheme is evaluated through practical implementation using a 1-bit RIS with 64×64 elements and software-defined radios operating within the 5G frequency band. The results demonstrate improved key-extraction performance and increased resistance to DoS attacks. The hardware implementation of the proposed approach further validated its effectiveness in enhancing key-extraction performance in terms of the key generation and mismatch rates, while reducing the effect of the DoS attacks on the network.