V2X Communication Research Articles

Free Space Optical Communications for Intelligent Transportation Systems: Potentials and Challenges

As one of the key technologies of the intelligent transportation system (ITS), vehicle-to-everything (V2X) communications have received increasing attention. Because of the broad and unlicensed spectrum, free space optical (FSO) communications have become a potential technology for V2X communications. In this article, the implementation of FSO communications is introduced first. Afterward, for the three advantages of FSO communications in ITS, the concrete principle and implementation approaches are illustrated in detail. First, based on the adaptive illumination schemes, the communication range is expanded without visual interference caused by illumination. Second, with the help of mode division multiplexing (MDM) and wavelength division multiplexing (WDM), the communication rate is further increased. Third, integrating sensing and communication enables vehicles to effectively obtain information and improve traffic efficiency and safety. In addition, applications of FSO communications in various communication scenarios in ITS are described. To satisfy the complex requirements of communication, illumination, and sensing in traffic scenarios, a deep learning-enabled heterogeneous system of FSO and radio frequency is proposed. Furthermore, future applications and open issues are provided for further investigation.

Design of Anomalous Reflectors by Phase Gradient Unit Cell-Based Digitally Coded Metasurface

In this letter, we propose the designs of 1-bit and 2-bit digitally coded metasurfaces, which achieve anomalous reflection-based beam-steering. First, we demonstrate the systematic design flow of coded metasurface using phase gradient digital unit cells to achieve anomalous reflection in the preferred direction for a normally incident plane wave. Initially, the design procedure is analytically implemented to get the phase profiles for 1-bit and 2-bit digitally coded metasurfaces having equal sizes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\lambda _{0} \times 10\lambda _{0}$</tex-math></inline-formula> (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{0}$</tex-math></inline-formula> is the free space wavelength at 5.9 GHz) for a specific reflection angle, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\theta _{r}=30^{0},\phi _{r}=0^{0})$</tex-math></inline-formula> , and subsequently, the far-field plots are generated. Furthermore, these phase profiles are used to generate 3D-CAD models of the 1-bit and 2-bit metasurfaces using CST, and the desired far-field patterns are obtained having half-power beamwidth (HPBW) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5^{0}$</tex-math></inline-formula> . It is observed that the 1-bit coded metasurface produces additional side-lobe levels, which are minimized by use of 2-bit coding. Finally, the proposed 1-bit and 2-bit coded metasurfaces are fabricated, and the anomalously reflected far-field beam is detected by the received power at desired reflection angle and comparing the results with a perfect electric conductor (PEC). The proposed design is suitable for V2X communications and can be scaled to other frequencies.

Energy Management Strategy Based on V2X Communications and Road Information for a Connected PHEV and Its Evaluation Using an IDHIL Simulator

Conventional energy management strategies (EMSs) of hybrid electric vehicles (HEVs) only utilize in-vehicle information, such as an acceleration pedal, velocity, acceleration, engine RPM, state of charge (SOC), and radar. This paper presents a new EMS using out-vehicle information obtained by vehicle to everything (V2X) communication. The new EMS integrates cooperative eco-driving (CED) guidance and an adaptive equivalent consumption minimum strategy (A-ECMS) based on V2X communication information and road information. CED provides a guide signal and a guide speed to the driver. It guides pedal behavior in terms of coasting driving, acceleration and deceleration, and target speed. A-ECMSs calculate the target SOC based on the simplified road information of the planned route and reflects it in the equivalent factor. An integrated driving hardware-in-the-loop (IDHIL) simulator is also built to prove the new EMS by integrating a V2X communication device, a VANET simulator, and a vehicle simulator. The IDHIL test results demonstrate the validity and performance of the proposed EMS in a V2X communication environment.

Election of MPR Nodes and Detection of Malicious Nodes Based on a Byzantine Fault in the OLSR Protocol Case of a Scale-Free Network

V2X (Vehicle-to-Everything) communications play a crucial role in enabling the efficient and reliable exchange of information among vehicles, infrastructure, and other entities in smart transportation systems. However, the inherent vulnerabilities and dynamic nature of V2X networks present significant challenges for ensuring secure and trustworthy communication. By enhancing the security of the OLSR (Optimized Link State Routing) protocol through secure MultiPoint Relays (MPRs) Selection, this research aims to provide a robust approach that enhances the overall security posture of V2X networks, enabling safe and secure interactions between vehicles and their environment. The proposed method is based on the Byzantine general’s problem, which is the principle used in blockchain. Compared to the classical flooding mechanism, this technique greatly reduces network traffic overhead and improves the efficiency of bandwidth utilization. The results demonstrated that the proposed algorithm performed better than the well-used UM-OLSR implementation. The outcome proved that our MPR election algorithm guarantees a better packet delivery ratio, and it also performs very well in the detection and isolation of malicious nodes, leading to increased security of the OLSR protocol control plane.

Vehicle-to-Blockchain (V2B) Communication: Integrating Blockchain into V2X and IoT for Next-Generation Transportation Systems

As smart transportation systems evolve, secure and efficient V2X communication between vehicles and infrastructure becomes crucial. This paper introduces a Vehicle-to-Blockchain (V2B) communication architecture, leveraging blockchain technology for transparent and decentralized interactions. Our work contributes to the integration of blockchain into V2X and IoT for next-generation transportation systems. We propose several novel blockchain use cases, including a blockchain-based vehicle ownership system based on the multi-token standard, a vehicle scoring system, blockchain–IoT integration, and a decentralized ticket management system for transportation services. The architecture addresses key aspects, such as data integration, validity, and secure messaging, and introduces a decentralized payment system and marketplace for transportation in smart cities. We specifically emphasize the technical implementation of smart contracts for these use cases, underscoring their role in ensuring robust and reliable interactions. Through our decentralized approach, we pave the way for a transformative transportation ecosystem that is adaptable, resilient, and capable of meeting the evolving needs of smart cities.

Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Informer-based QoS prediction for V2X communication: A method with verification using reality field test data

Vehicle-to-everything (V2X) communication plays a critical role in connected and automated driving applications, which requires strict Quality of Service (QoS) performance in terms of delay and reliability since human safety is involved. The QoS of V2X communications is impacted by various factors, such as radio interference, mobility, and user equipment (UE) density, making accurate prediction challenging. Various methods have been exploited to predict QoS deterioration. However, they usually require high computational complexity or may not capture the complex features of V2X communication environment. In this paper, a novel Informer-based QoS prediction model with a causal convolution self-attention mechanism is presented, which achieves high prediction performance with minimal computing resources. Notably, the test data used in the evaluation of the model is collected from the National Intelligent Vehicle and Intelligent Transportation Demonstration Zone in Daxing District, Beijing. The experimental results demonstrate that the proposed method exhibits superior accuracy and stability compared to existing methods such as Back Propagation (BP), ELMAN, LSTM, and CNN.

A joint cluster-based RRM and Low-latency framework using the full-duplex mechanism for NR-V2X networks

The radio resource management (RRM) problem in new radio vehicle-to-everything (NR-V2X) communication systems is a combinatorial optimization problem, and it is hard to achieve an optimum result in polynomial time. To reduce the complexity, linear algorithms or meta-heuristics can solve the problem. In this study, we proposed a joint cluster-based resource management and low-latency framework using a full-duplex mechanism (JCRRM-FD) for NR-V2X networks, which is used for vehicle-to-vehicle (V2V)/ device-to-device (D2D) and vehicle-to-infrastructure (V2I) transmission in NR-V2X networks. The delay, resource management, and system throughput analysis of the full-duplex mechanism in this framework was performed to deal with resource utilization and latency constraints. Moreover, joint cluster-based radio resource management and ant colony optimization (ACO) algorithms were proposed to manage and optimize resources efficiently to achieve user separation. The swarm intelligence algorithm is a standard meta-heuristic algorithm employed to deal with the optimization concern by exploiting the V2X communication network’s unlimited speed (maximizing the data transfer capacity and overall network performance) while considering the quality-of-service (QoS) requirements. A comprehensive experiment analysis was enacted to evaluate the efficiency of the developed JCRRM-FD framework with baseline approaches. Based on the simulation results, the proposed JCRRM-FD framework enhances the fairness index, average delay, packet drop rate, best cost value, CDF, and throughput compared to the benchmark approaches.

A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks

Resource management in the next-generation vehicle-to-everything (V2X) communication networks is a demanding research problem. It is difficult to achieve the best results if the resources are not managed efficiently. To address this challenge and facilitate the dynamic movement of network demands, efficient management of resources is crucial. In this context, we propose a duplex deep reinforcement learning (DDRL)-based resource management framework for next-generation V2X communication networks. Our framework considers multiple network state parameters and utilizes these resources during the learning phase to address resource management problems effectively. The key objective was to select the forthcoming resource control state through the duplex deep reinforcement learning phase, thereby optimizing resource utilization efficiency. To achieve this, we jointly implement time division duplexing (TDD) and frequency division duplexing (FDD), which allow for adaptive management of TDD and FDD to meet high mobility requirements and efficiently utilize limited radio resources. This study introduces a dynamic duplex deep Q-learning-assisted sequential decision-making algorithm to manage band resources for the next-generation V2X network. By employing this algorithm, we enable the efficient allocation of resources, considering factors such as latency, system throughput, network utilization, busy channel rate (BCR), and packet received rate. To validate the performance of our proposed framework, we conducted simulation experiments using benchmarking techniques. The results demonstrate that our framework outperforms benchmark approaches across various performance parameters. It exhibits superior efficiency in terms of latency, system throughput, network utilization, BCR, and packet received rate.

Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system

To achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging technology. The RIS-assisted mmWave massive MIMO vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of RIS-assisted mmWave V2X communications is though not exempt of challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted mmWave massive MIMO system to improving the safety and traffic efficiency of cooperative automated driving. The received signal of the vehicle is decomposed into four-dimensional tensors and study the channel, frequency and time attention (CFTA) algorithm using the low order tensor characteristics of mmWave channels. This algorithm can extract the frequency and time characteristics of the channel, and obtain the millimeter wave characteristic channel between the corresponding base station (BS) and the vehicles. The problem of jointly optimization of beamforming matrix at the BS and the phase shift matrix on the RIS is constructed and solved using deep reinforcement learning (DRL). Unlike traditional alternating optimization methods, the integration of DRL techniques into the optimal design of the system enables the observation of immediate rewards, learning from the environment and improving it to obtain optimal solutions to high-dimensional jointly beamforming optimization problems ultimately. The simulation results show that the proposed algorithm has meaningful performance compared with other methods.

Mutual Coupling Reduction of a Multiple-Input Multiple-Output Antenna Using an Absorber Wall and a Combline Filter for V2X Communication.

This paper presents an MIMO antenna for vehicle-to-everything (V2X) communication, which adopts two ways of combline filters and absorption wall decoupling. A combline filter and an absorption wall are used, respectively, for internal and external decoupling. The combline filter is incorporated between the ground of the two adjacent antennas, which reduces the mutual coupling between them. Additionally, the mutual coupling of radiation between adjacent antennas is significantly reduced by the absorber wall. These combline filters and absorber walls use the method of electromagnetic field distribution to explain the reduction in the mutual coupling between the adjacent antennas. The transmission coefficient and surface current distribution explain the effectiveness of the decoupling structure. When the frequency is between 3.8 and 4.8 GHz, the simulation and measurement results show that S11 is less than -10 dB, the bandwidth is 25% and the peak gain is 7.8 dBi. In addition, the proposed MIMO antenna has a high isolation between antenna units (>37 dB), and the envelop correlation coefficient (ECC) is less than 0.005.

Maximum Sum Rate of MCM–NOMA in Future Vehicular Sensor Networks

This letter addresses the challenge of determining the maximum sum rate achievable in high mobility scenarios with time and frequency selectivity in 5G V2X communications, particularly in the context of IoT/sensor networks. We derive a closed-form expression for the maximum sum rate of a multicarrier non-orthogonal multiple access (NOMA) system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> users, and validate our theoretical results through simulations by comparing them with traditional orthogonal schemes. Our work provides valuable insights into the potential of NOMA for high-speed V2X communications, and offers a novel contribution to the existing literature by presenting a closed-form expression allowing the development of efficient and reliable 5G V2X systems.

3GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach

Vehicle-to-everything (V2X) communications is an emerging branch of wireless communications and has recently gained a lot of interest not only from academia but also from the automotive and telecommunications industry. An important feature of V2X telecommunication systems is that the communication quality varies quickly in time due to the high mobility of the transceivers and due to the rapid change in the fading characteristics of the communication medium. For this reason, it is vital to maintain tolerable quality of service (QoS) in a V2X communication environment, in terms of communication reliability, power, end-to-end latency, data rate, communication range, throughput and vehicle density. A significant impact on the quality parameters of the system has been the coding scheme used by the V2X telecommunication system. For this purpose, we examine the effect that popular coding schemes have on the QoS of a V2X communication system. More specifically, the impact of fourth-generation long-term evolution (4G-LTE) turbo codes, fifth-generation new radio (5G-NR) polar codes and 5G-NR low-density parity-check codes (LDPC) on the QoS parameters of a V2X communication system is researched. Το this end, we employ stochastic V2X propagation models that simulate every possible V2X channel state and traffic environment, based on the third-generation partnership project (3GPP) Release 16 specifications. These propagation models form the basis of our study, since through them, at a first level, we analyze the frame error rate (FER) performance for different levels of signal-to-noise ratios (SNRs) for all the aforementioned coding schemes and 128-bit V2X-compatible data frames, and, at a second level, we investigate the impact of these propagation models and coding schemes on the V2X QoS parameters. Our analysis shows that turbo-based coding schemes satisfy all the QoS parameters and achieve overall communication quality comparable to polar and better than LDPC, making them suitable for small-frame 5G V2X services.

Integrated sensing and communication‐promoted beam tracking and coverage for complex vehicular networks

AbstractThe future vehicle‐to‐everything (V2X) scenarios greatly call for reliable and efficient communication links between roadside units (RSUs) and vehicles. Integrated sensing and communication (ISAC) represents one of the key candidate technologies for 6G, which provides new solutions for this problem. In this paper, the beam tracking design for V2X communication is investigated by exploiting the ISAC technique, where the RSU uses the echoes of the ISAC signals to estimate the motion parameters of the vehicles. Compared with traditional feedback‐based beam tracking, the proposed method can reduce the overall signaling costs and improve communication robustness. Furthermore, to achieve high accuracy and low latency for beam alignment, a Trajectory Adaptive Unscented Kalman Filter (TA‐UKF) beam tracking algorithm is proposed, which obtains the optimal state transition model following the change of the vehicle state. Considering practical application scenarios, an ISAC‐based adaptive beam coverage (ISAC‐AC) scheme is further developed for the beam tracking of extended targets, which can achieve the beam misalignment of vehicles in practical scenarios. Numerical results demonstrate that the proposed beam tracking and coverage strategies outperform baseline approaches.

Energy-Efficient Resource Allocation for D2D-V2V Communication with Load Balancing

The significance of vehicle-to everything (V2X) communication in ensuring road safety is undeniable. In addition, real-time vehicle communication requires an ample amount of spectrum resources. However, the existing spectrum resources are seriously scarce, and the utilization rate is not high, leading to high delays in V2X communication and other unfavorable factors in the case of fast-moving vehicles, bringing great safety risks to driving. Load balancing is one of the most effective methods to improve spectrum utilization. However, the existing load balancing schemes merely focus on static conditions, with a lack of joint scheduling schemes, which cannot support the communication framework of dynamic V2X. To address both of these issues, in this paper, a new communication method is proposed. In addition, this paper studies a joint load balancing scheme of mobility vehicle-to-vehicle (V2V) and user association under incomplete channel state information (CSI) and realizes the load balancing management of a cross-cell V2X network. An algorithm combining power control and resource allocation mode selection is proposed. In particular, according to different coverage areas, different allocation algorithms are adopted to maximize the overall system efficiency. The simulation results show that this strategy can maintain low latency and effectively improve the system energy efficiency of vehicle users.

Power Control and Fuzzy Pairing in V2X Communications

In this article, a joint power control and pairing-based resource allocation problem in the vehicle-to-everything (V2X) network are studied. This V2X network consists of a vehicle-to-infrastructure (V2I) link and a vehicle-to-vehicle (V2V) link. Therefore, the resource allocation problem has been formulated with the objective function of maximizing the V2I link's rate with the constraints of quality of service. Due to its NP hard nature, the original resource allocation problem is divided into two subproblems of power control and pairing. The power control subproblem is transformed into a convex problem using an exponential variable change. A fuzzy decision-making method is exploited to solve the pairing subproblem to have a highly reliable and stable V2V link. Then, the proposed joint power control and pairing-based resource allocation technique is evaluated with a simulated highway and compared with similar pairing algorithms. These performance comparisons reveal that the stability of the proposed resource allocation technique outperforms the other related methods.

MmWave Channel Stationarity Analysis of V2X Communications in an Urban Environment

Vehicular communication channels are subject to high non-stationarity mainly characterized by the scatterers' and/or transceivers' high mobility. In this sense, this work presents a characterization of the channel quasi-stationarity regions (QSR) in a V2X generic high-dense urban environment at millimeter wave (mmWave) frequencies (28 GHz). Results are extracted from multiple snapshot simulations by means of a three-dimensional ray-launching (3D-RL) algorithm inducing the continuous mobility of the vehicles on the scene and validated with an experimental campaign of measurements in the real scenario. The average power delay profile (APDP) correlation matrix is used as a descriptor of the channel non-stationarity and the mean correlation is outlined for several thresholds. The obtained QSR results are consistent with the related works reported in the literature. Finally, the effects of these QSR in small- and large-scale parameters are assessed as per threshold considerations.

A survey on efforts to apply IPv6 in V2X communication networks

This survey focuses on the application possibilities of using Internet Protocol (version 6) in Vehicle-to-Everything (V2X) networking architectures by analyzing existing standards and related papers in this field. The article explains the terminology used in IP-based V2X networks, introduces the considered use cases, and gives an overview of the three standardized options applying IPv6 in vehicular environments: IEEE WAVE provides networking services to applications in vehicular networks through IPv6, IPv6 over 802.11-OCB can be implemented in Wi-Fi-based ad hoc vehicular networks for both V2V and V2I, and the Geo Networking IPv6 adaption sub-layer (GN6ASL) for IPv6 support in the ETSI ITS protocol family. The paper also highlights non-standardized solutions and available techniques designed for IPv6-based V2X infrastructures, summarizes wireless connection requirements, and mobility management needs, together with the newest research efforts aiming at the applications of IPv6 in V2X communications.

Highly decoupled and high gain conformal two-port MIMO antenna for V2X communications

This research provides an illustration of a straightforward, budget-friendly, and low-profile multiple-input multiple-output (MIMO) antenna that is able to function at 5.9 GHz and has lowered mutual coupling between the antenna parts. The frequency spectrum of this antenna for automotive communications ranges from 5.85 GHz to 5.95 GHz. It is created specifically for this range. The suggested antenna has not one but two distinct radiating components, each of which is slit in order to give a significant amount of isolation between the radiators. The MIMO antenna has a total dimension of 60 × 87 mm2, which is notably tiny for Vehicular to anything (V2X) applications. Additionally, the MIMO antenna is printed on a 0.6 mm thick flexible polymide substrate. The most important takeaway from this study is that it is possible to achieve extremely low isolation (less than 28 dB) over the entire operational range. The suggested antenna for V2X communications has a radiation efficiency of around 95%, which allows it to achieve an Envelope Correlation Coefficient (ECC) of less than 0.001. The suggested configuration is analyzed in terms of S-parameters, radiation properties such as a two-dimensional radiation pattern, efficiency, and gain, as well as MIMO parameters. With such low-profile design the designed antenna achieves significant improvement in isolation radiation performance in the desired band of operation without adopting decoupling mechanism proves that the conformal antenna that is suggested in this study has been shown to be an important contender for vehicular communications.

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.