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Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles' sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors' data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window ( L w ), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle. [ABSTRACT FROM AUTHOR]

Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements.

In recent years, connected vehicle technologies emerged to provide a safer and more connected environment for transportation systems, which has further evolved into cellular vehicle-to-everything (C-V2X) technology. The current study contributes in the direction of future research of smart India @2047 in connected vehicles. The study presents the diverse aspects of 5G V2X as a comprehensive overview of Vehicle-to-Everything (V2X) technology that covers the significant scope of V2X technology, including its development, components, functionality, use cases, and future potential. It is observed that few surveys are available that cover limited aspects of 5G V2X communication technologies, with one or two preferred architectures, and only few provides the definitions of 3GPP use cases. Emergency vehicles (EMV) are identified as an important part of an emergency service, and the main goal of EMV is to reduce the response time. The study discusses the research challenges in the field of V2X and presents future directions to reduce the probability of untoward incidents and enhance the EMV response time. The work leverages by synthesizing the latest research in 5G V2X technological advancements like Multiple Input Multiple Output, Beamforming, and Integrated Sensing and Communication, highlighting the roles of 5G architectures and providing a deeper understanding of the 5GAA V2X use cases from the perspective of societal benefit. The study provides a comprehensive future research directions to the researchers on 5G V2X technologies and current research trends for smart EMV applications.

To meet the communications demands of connected vehicles, the wireless devices deployed in vehicles and on roadside infrastructure may need access to more spectrum than is available today. This paper proposes a novel approach that allows connected vehicle devices using V2X technology (e.g., C-V2X or NR-V2X) to share spectrum with Wi-Fi and other unlicensed devices, thereby gaining access to more spectrum. The proposed approach requires no change to Wi-Fi technology so there is no need to replace Wi-Fi devices that have been deployed, and only modest modifications to V2X which reduces cost and complexity. It uses a backward-compatible form of beaconing. Unlike previous work, the resources allocated to V2X are dynamically adjusted for greater efficiency. The approach also does not require involvement from a cellular operator or other centralized controller. One spectrum band where this approach could be especially beneficial is adjacent to the Intelligent Transportation System (ITS) band, where this approach could help meet the needs of both connected vehicles and Wi-Fi 6. Simulation results show that it is possible to protect quality of service for both V2X and Wi-Fi communications in a shared band, while greatly improving spectrum efficiency. This paper also describes steps that standards bodies (IEEE 802.11 and 3GPP) and spectrum regulators could take to advance this spectrum-sharing approach.

Transitioning to more intelligent, autonomous transportation systems necessitates network infrastructure capable of accommodating both substantial uplink traffic and massive vehicle connectivity. Current approaches addressing these throughput and connectivity requirements rely on the utilization of the multiple input, multiple output (MIMO) technology. However, When traditional linear detection/precoding processing methods are adopted, they require the deployment of an extensive number of co-located, access-point antennas to support a comparatively much smaller number of data streams. Such a setup significantly increases the power consumption on the radio side, raising substantial concerns about the operational costs and sustainability of such deployments, particularly in densely deployed scenarios, across extensive road networks. Addressing these concerns, this work proposes an Open Radio Access Network (Open-RAN) deployment that incorporates a Massively Parallelizable, Nonlinear (MPNL) MIMO processing framework and assesses, for the first time, its impact on the power consumption and vehicular connectivity in various Vehicle-to-Infrastructure (V2I) and Network (V2N) scenarios. We show that flexible, Open-RAN physical layer deployments, incorporating MPNL, emerge as a critical power efficiency enabler, especially when flexibly activating/deactivating employed RF elements. Our field-programmable gate array (FPGA) based evaluation of MPNL, reveals that it can lead to significant power savings on the radio side, by eliminating the need for a “massive” number of base station antennas and radio frequency (RF) chains. Additionally, our findings show substantial connectivity gains, exceeding 400%, in terms of concurrently transmitting vehicles compared to traditional processing approaches, without significantly affecting the access point power consumption budgets, thereby catalyzing the evolution towards more intelligent, fully autonomous, and sustainable transportation systems.

Cellular Vehicle-to-Everything (C-V2X) has become one of the most anticipated technologies for vehicular safety network. In LTE C-V2X Basic Safety Messages (BSMs) are transmitted on radio resources that are allocated with a periodic resource reusability. This allocation is based on a semi persistent sensing-based scheduling scheme (SPS) algorithm. But, due to this reuse of periodic resources, the possibility of loss of consecutive packets between the same vehicle pair is significant. This study discusses different approaches proposed to solve this consecutive loss problem. Based on this investigation, this article suggests an efficient One-Shot based solution with a new control parameter, that performs superior to the state-of-the-art solution that is standardized in SAE J3161/1 which this article analyzes and shows to have limitation in case of high-density scenario.

Advances in connectivity and computing infrastructure facilitate the introduction of innovative Cooperative Intelligent Transport Systems (C-ITS) services. However, meeting the requirements of these highly demanding services calls for novel computing architectures that handle extensive device connections, minimize latency, and support multiple resource-intensive services concurrently. To overcome these challenges, this work presents an architecture that comprises three layers: 1) on-board unit (OBU) mainly as a data producer; 2) intermediate edge layer where low-latency backend services can be deployed; and 3) cloud layer for non-real-time backend services. The OBU software stack implements the ETSI C-ITS standard and supports multicast over the cellular network. The edge layer includes an in-memory database, and the cloud layer a persistent database. Each layer has its own Application Programming Interface (API) for data consumption. We conducted several experiments to demonstrate the feasibility of our proposed system that ensures scalability and interconnection between vehicles, edge and cloud servers. We also assess the delay caused by each of the elements of the architecture, and we discuss the potential solutions for the identified issues.

SDN’s ability to provide a global view, centralized control, and flexibility in orchestrating network infrastructure are expected to overcome challenges in the dynamic conditions of vehicular communications. We are looking to rely on SDN technologies to achieve Vehicular communications capacity and performance, which increase the safety of vehicles and transportation, save cost & energy, and increase the vehicles’ autonomy level. The usage of SDN in vehicular communications has been the subject of considerable research. Researchers address challenges such as low latency, high throughput, reliability, dense VANET, security, and scalability. Furthermore, this paper surveys the integration of SDN and other enabling technologies with vehicular communications, both for IEEE 802.11 and the C-V2X RAT families. It begins by discussing performance comparisons and coexistence between IEEE 802.11 and C-V2X standards, implementation of SDN in various use cases, integration of SDN with other enabling technologies, the study of particular SDN components to support vehicular communications performance, and SDN usage to support the specific issues vehicular communications. Finally, open directions and challenges of the research are discussed.

To reduce the impact of the emergency vehicle preemption strategy on vehicles in non-priority phases at intersections, an emergency preemption green light compensation model in C-V2X environment was proposed.The proposed model doesn’t affect the emergency vehicle preemption strategy.The green time of the non-priority phases was calculated according to the real-time traffic demand to minimize the adverse impact on vehicles in non-priority phases.The proposed model was verified with simulation using an emergency vehicle.The results demonstrate that the proposed method can effectively decrease the queue lengths and reduce the average delay in non-priority phases after applying the emergency vehicle preemption strategy.The proposed method can improve traffic efficiency by optimizing the signal timing after the emergency vehicle passes through an intersection.

This paper presents Bottom-up Residual vector quantization for learned Image Compression (BRIC). This novel deep learning-based image compression method quantizes latent representations beginning with the lowest resolution and proceeding with residual quantization to the highest resolution. BRIC outperforms the top-down approach from prior work, reducing average bit rates for a given image quality on three vehicular datasets. BRIC is designed with a shallower architecture than prior work, which enables faster encoding/decoding speeds and lower peak memory usage, both critical for practical application. The importance of BRIC is further demonstrated in vehicular networks, where reduced bit rates can double frame delivery rates under challenging channel conditions in a C-V2X network and significantly increase the probability of achieving target frame rates in 5G networks. This bottom-up quantization approach inherently captures a notion of feature importance, making it well-suited for future incorporation into semantic communication systems that utilize unequal error protection during transmission. Overall, BRIC represents an important step towards enabling efficient, reliable, and scalable visual data sharing on the roadway, significantly enhancing the performance of vehicular networks.

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

A distributed congestion control must be adaptable to varying target communication ranges as cellular V2X (C-V2X) is evolving to support flexible coverage suitable for various service scenarios. This study proposes range-adaptive distributed power control (Ra-DPC) based on deep reinforcement learning (DRL) with the Monte Carlo policy gradient algorithm. A key finding is that the agents learn Ra-DPC more effectively when the cumulative interference power of the subchannels is adopted as the state of the DRL model, rather than the channel busy ratio. The proposed Ra-DPC algorithm performs better in energy efficiency and packet delivery ratio than the existing technologies.

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integration of Cellular Vehicle-to-Everything (C-V2X) and Mobile Edge Computing (MEC) is critical for satisfying the demanding requirements of vehicular applications, which are characterized by ultra-low latency and ultra-high reliability. In this paper, we address the challenge of jointly optimizing computation offloading and resource allocation in C-V2X network. To achieve this, we propose a hierarchical MEC/C-V2X network that accounts for the dynamic changes of the vehicular network and the diversity of computation offloading patterns. Additionally, we establish a collaborative computation offloading model that supports multiple offloading patterns. We formulate the dynamic computation offloading and resource allocation problem as a sequential decision problem based on the Markovian decision process. To enable automated and intelligent decision-making, we propose a deep reinforcement learning algorithm called ORAD, based on the deep deterministic policy gradient algorithm, to maximize offloading success rate in real-time. The numerical results demonstrate that the proposed algorithm effectively provides the optimal policy, resulting in the offloading success rate of vehicular tasks being improved by 2.73% to 95.51%. [ABSTRACT FROM AUTHOR]

Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems' constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads. [ABSTRACT FROM AUTHOR]

With the evolution of vehicle-to-everything (V2X) technology, autonomous driving (AD) has entered a new stage of development, called vehicle-infrastructure cooperative autonomous driving (VICAD).Through the integrated complex cyber-physical system formed by the deep integration of “human-vehicle-road-cloud”, cooperative perception, cooperative decision-making and planning, and even cooperative control can be achieved with autonomous vehicles (AV), enhancing the safety of autonomous driving and helping to overcome various complex traffic environments.The concept and overall architecture of the complex cyber-physical system of VICAD was firstly introduced, and a series of typical application scenarios, technical principles, C-V2X performance requirements, and functional and performance requirements of the VICAD system were proposed.It can provide a reference and solution for the deep integration development of intelligent networked vehicles and intelligent transportation in the next stage.

For the sidelink resource autonomous selection scheme of different priority services in celluar-vehicle to everything (C-V2X) system, the procedure of autonomous selection algorithm based on reference signal energy was analyzed, and the energy threshold equation was designed.For the energy equation parameter estimation problem, energy-based autonomous selection algorithm was combined with deep Q learning algorithm, and the optimal parameter value of the energy threshold equation was obtained by iteration of the finite-degree algorithm.Simulation results showed that the side resource allocation algorithm based on deep Q learning could ensure the side resource requirements of V2X services with different priorities, and improve the packet reception ratio performance of the system.

Intelligent transportation systems have recently become a promising technology for future industry to provide safe, green, and automated driving. In this regard, the Third Generation Partnership Project (3GPP) has proposed to utilize cellular communication to enable direct communication between vehicles with and without cellular infrastructure assistance. 3GPP has introduced the Sensing-Based Semi-Persistent Scheduling (S-SPS) technique when coverage of the cellular system is absent. S-SPS faces resource collision and performance degradation problems when channel load increases in the network. This paper suggests a decentralized congestion control and transmission power control mechanism (TPC-DCC) with an adaptive threshold for the received signal as a combination method to decrease channel load in the network. Furthermore, this work introduces a novel channel load adjustment. The new adjusting algorithm is based on a constant difference between the upper and lower boundaries of channel load at each level to handle channel overload and provide more flexibility using DCC mechanisms. The interactions of the proposed algorithm with S-SPS and the Extended-Estimation Reservation Resource Allocation (E-ERRA) algorithms that the authors previously proposed are investigated. The results indicate that system performance can be substantially improved when the transmission power and reception threshold are adaptively adjusted to the proposed channel load adjustment. The results of E-ERRA with the proposed channel load adjustment method show promising results compared to S-SPS.

Cooperative transit signal priority (CTSP), which integrates cellular vehicle-to-everything (C-V2X) technology, can improve the efficiency of connected transit vehicles at signalized intersections. However, few studies have considered the impact of bus stops on CTSP under adaptive signal control. Near-side bus stops at an intersection and multiple transit vehicles arriving at the same bus stop affect the bus arrival prediction. To overcome these shortcomings, we propose a Cooperative Transit signal priority method under Adaptive signal control considering bus Stops (CTAS). The queue prediction model is built with the real-time traffic data from the roadside perception system at intersections. The distance prediction model is built with real-time transit vehicles data, queue length, location of the bus stop, dwell time, and the preceding transit vehicles at the bus stop. Then the optimal signal timing plan is obtained by minimizing the total person delay. Finally, the rolling horizon strategy is utilized to continuously adjust the signal timing plan to adapt to changing traffic conditions. We verify the proposed method using measured data from an intersection in Changsha. The results indicate that our method achieves the best performance in terms of bus delay and person delay compared with the existing methods. It is an effective method to improve efficiency for transit vehicles and passengers.

Cellular vehicle-to-everything(C-V2X) technology can protect vulnerable road users(VRUs) in traffic accidents. Smartphones are the most common portable devices for VRUs, but they do not support direct communication between vehicles and pedestrians. To address this problem, we propose a vehicle-to-pedestrian collision warning system that includes a phone case designed for the smartphone. It allows VRUs to communicate directly with connected vehicles(CVs) through C-V2X. A collision warning method based on s-t-coordinates is proposed to help VRUs to avoid collision with CVs on both straight and curved roads. Firstly, the World Geodetic System-1984(WGS-84) coordinates of the VRU and CV are transformed to Universal Transverse Mercator(UTM) coordinates. Then the s-t-coordinates of the VRU and CV are calculated. The lateral and longitudinal distance between them is determined. Finally, the time-to-collision(TTC) is used to determine the time to alert the VRU and the driver. To verify the effectiveness of this system, we conduct experiments on communication, positioning, and scenario. The communication experiments show that the system can communicate effectively with low latency and its reliable communication range is more than 200 meters in different scenarios. The positioning experiments show that the average positioning accuracy of the phone case is 0.83 meters in the open-sky scenario, which satisfies the lane-level positioning requirement for VRU applications. The scenario experiments indicate that our method can alert the driver and VRU on both straight and curved roads. The comparative experiments illustrate that our method outperforms the conventional method(CM) and the existing pedestrian alert system(PAS).

A helical antenna for Cellular-Vehicle To Everything (C-V2X) communications is manufactured and integrated around the car high mount stop light (CHMSL) area of a Ford SUV vehicle. The antenna performance is measured on a foam pillar, on a ground plane and on the vehicle, inside a near field measurement system. The antenna consists of three helical arms wrapped around a 3D-printed hemisphere with a radius of 4.8 mm. The helical arms are built using commercial wire with a radius of 0.127 mm. It operates in a monopole configuration, has an omni-directional radiation pattern and vertical polarization. The maximum measured realized gain of the antenna when integrated on the vehicle is 2.5 dBi at 5.9 GHz.

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. [ABSTRACT FROM AUTHOR]

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced (LTE-A), which was developed by 3rd Generation Partnership Project (3GPP). Device-to-device (D2D) communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B (eNB). Moreover, D2D transmission can support vehicles as a vehicle-to-vehicle (V2V) environment. It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety (and efficiency). However, radio resources are limited in radio networks. A vehicle transmits through D2D in Long Term Evolution-Vehicle (LTE-V) mode-3 standard, and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously. When using the same radio resources, the probability of interference may increase. This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network. Moreover, resource allocation under different cycles was analyzed, and a scheme for resource selection is proposed based on cycle size. The proposed Semi Persistent Gain Aware Resource Allocation (SP-GARA) scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance. [ABSTRACT FROM AUTHOR]

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In this paper, we investigate the resource block (RB) allocation problem in cellular vehicle-to-everything (C-V2X) networks mode 3, where the cellular networks schedule the RBs for direct vehicular communications. First, we establish the communication model and introduce the effective capacity and queuing theory to describe the reliability of vehicle-to-vehicle (V2V) links. Then, we introduce the α -fair function and formulate the joint power control and RB allocation problem considering the allocation fairness and the different quality-of-service (QoS) requirements for vehicle-to-infrastructure (V2I) and V2V links. Our objective is to maximize the sum capacity of all V2I links with the α -fair function while guaranteeing the allocation fairness among V2I links and the transmission reliability for each V2V pair. To achieve this objective, we propose a novel matching game theory algorithm based on the exchanged preference profiles between the two participant sets, i.e., V2V and V2I links. Simulation results show that our proposed algorithm is adaptive to the dynamic vehicular network and achieves better efficiency and fairness trade-offs, outperforming the classic allocation method. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings). [ABSTRACT FROM AUTHOR]

C-V2X (cellular vehicle-to-everything) is the international standards dominated by China’s enterprises for V2X wireless communications.Based on a brief review of C-V2X, it was indicated that C-V2X could provide capabilities such as low-latency and high-reliability for communications between vehicles and the surrounding traffic elements.C-V2X has demonstrated an obvious surpassing international technological trend and industrial competitions because of technical advantages.The related concepts of V2X, the relationship between 5G and C-V2X, the relationship between V2X and vehicle-infrastructure cooperation, the relationship between V2X and self-driving vehicles as well as the relationship between individual vehicle with intelligence and connected vehicles with cooperative intelligence, were clearly clarified.The insights about the development of C-V2X and intelligent connected vehicles (ICV) were presented.Facing the great strategic opportunities of global automobile industry revolution and China’s transportation industry reform, the innovative development mode of vehicle-infrastructure-cloud cooperation based on “5G+C-V2X” was proposed accordingly.Finally, the industrial progresses and problems were discussed with corresponding policy suggestions.

Vehicle edge computing (VEC) enhances the distributed task processing capability within intelligent vehicle-infrastructure cooperative systems (i-VICS) by deploying servers at the network edge. However, the proliferation of onboard sensors and the continual emergence of new applications have exacerbated the inadequacy of wireless spectrum resources and edge server resources, while the high mobility of vehicles reduces reliability in task processing, resulting in increased communication and task processing delays. To address these challenges, we propose a mobile-aware Many-to-Many Parallel (MTMP) offloading scheme that integrates: a) millimeter-wave (mmWave) and cellular vehicle-to-everything (C-V2X) to mitigate excessive communication delays; and b) leveraging the underutilized resources of surrounding vehicles and parallel offloading to mitigate excessive task processing delays. To minimize the average completion delay of all tasks, this paper formulates the objective as a min-max optimization problem and solves it using the maximum entropy method (MEM), the Lagrange multiplier method, and an iterative algorithm. Extensive experimental results demonstrate the superior performance of the proposed scheme in comparison with other baseline algorithms. Specifically, our proposal achieves a 47 % reduction in task completion delay under optimal conditions, a 31.3 % increase in task completion rate, and a 30 % decrease in program runtime compared to the worst-performing algorithm. [ABSTRACT FROM AUTHOR]

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

In this paper, we introduce an electronically tuneable band-stop filter based on a dielectric resonator, along with its design principles and equivalent circuit model aimed at the coexistence of intelligent transport systems (ITS) and cellular vehicle-to-everything (C-V2X) communications operating at 5.9 GHz, with the widely spread vehicular dedicated short range communications (DSRC) at 5.8 GHz. The proposed architecture involves a dielectric resonator coupled via a microstrip transmission line to a planar ring resonator. The resonance is electronically tuned by varying the bias voltage of a varactor diode placed in the ring. This design is analyzed theoretically and experimentally. For validation, a filter operating at 5.8 GHz was designed. The prototype is capable of enhancing coexistence exhibiting to features. First, when inserted into the ITS transmitter chain, it reduces unwanted transmitter emissions in the out-of-band spectrum occupied by the DSRC carriers in the 5.8 GHz frequency band of at least 25 dB, with a minimal insertion loss at 5. 9 GHz of 1.6 dB; secondly, when inserted in the ITS receiver chain, it exhibits high linearity that prevents the generation of intermodulation products falling into the filter pass band. The prototype features more than 21 dBm of third-order input intercept points, and a tuning range of 26 MHz while maintaining a minimal loaded Q factor of 93. The prototype is discussed regarding the detailed equivalent circuit parameter extraction and their dependency upon the control voltage. [ABSTRACT FROM AUTHOR]

With an increasing number of vehicles, there is a need for reliable and secure communication between subjects participating in the dynamic traffic environment. This kind of communication is called V2X (Vehicle-to-Everything). It can also contribute to higher level of autonomous driving. The V2X model operation is enabled by use of the newest technologies and standards, defined by 3GPP Organization. In this paper, a review of the V2X communication models, available technologies and standards are studied. [ABSTRACT FROM AUTHOR]

In the IoV system, the high speed movement of vehicle nodes and frequent changes in speed, direction and other states cause time-varying fading of the wireless channel in vehicular networks. To solve the problems of the currently used adaptive traffic beacons (ATB) prediction algorithm process with complex and many channel quality evaluation indicators, a new R-ATB prediction algorithm was proposed. The R-ATB algorithm was achieved by calculating the corresponding correlation time parameter of relevant time (RT) according to the Doppler shift degree of node motion. The package delivery rate (PDR) and the time delay parameters under the correlated channel prediction algorithm were analyzed and evaluated by setting the conditions and system modeling in OMNET++ universal network platform. The results show that compared with static beacons and traditional ATB algorithms, in the proposed R-ATB prediction algorithm, less amount of real-time evaluation metrics and smaller computation of the prediction process are used to reduce the communication channel congestion of IoV. The propagation timeliness of security beacons and the PDR of high-speed data transmission success rate are improved in typical high-density OBUs data interaction scenarios. [ABSTRACT FROM AUTHOR]

Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path data, user driving habits data, and so on. It is necessary to ensure the opposite equipment is registered C-V2X equipment (installed in the vehicle), and the data transmitted between the equipment is secure. This paper proposes a V2V identity authentication and key agreement scheme based on identity-based cryptograph (IBC). The C-V2X equipment use its vehicle identification (VID) as its public key. The key management center (KMC) generates a private key for the C-V2X equipment according to its VID. The C-V2X equipment transmit secret data encrypted with the opposite equipment public key to the other equipment, they authenticate each other through a challenge response protocol based on identity-based cryptography, and they negotiate the working key used to encrypt the communication data. The scheme can secure the V2V communication with low computational cost and simple architecture and meet the lightweight and efficient communication requirements of the C-V2X system. [ABSTRACT FROM AUTHOR]

In this analysis, Cooperative Intelligent Transportation System relevant scenarios are created to investigate the need to differentiate Vehicle-to-X transmission technologies on behalf of accident analysis. For each scenario, the distances between the vehicles are calculated 5 s before the crash. Studies on the difference between Dedicated Short-Range Communication (IEEE 802.11p) and Cellular Vehicle-to-X communication (LTE-V2C PC5 Mode 4) are then used to assess whether both technologies have a reliable connection over the relevant distance. If this is the case, the transmission technology is of secondary importance for future investigations on Vehicle-to-X communication in combination with accident analysis. The results show that studies on freeways and rural roads can be carried out independently of the transmission technology and other boundary conditions (speed, traffic density, non-line of sight/line of sight). The situation is different for studies in urban areas, where both technologies may not have a sufficiently reliable connection range depending on the traffic density. [ABSTRACT FROM AUTHOR]