Vehicle-to-everything Research Articles

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.

Review of C-V2X Research on Intelligent Connected Vehicles

With the rapid development of society, all walks of life are more pursuing efficiency and safety, so is the development of automobiles. When driving a car, there are many subjective factors, there will be a lot of visual blind areas and road information, which is easy to cause safety accidents and traffic congestion. Nowadays, with the gradual promotion of autonomous driving, C-V2X (Cellular Vehicle-to everything) technology can well realize the interconnection between cars and cars and between cars and transportation facilities, which can not only warn drivers of dangers in advance, but also help promote the development of autonomous vehicles. This paper first describes the concept of c-v2x and the related equipment of the network. Combining the development technology of the network owner of the network, the DSRC vehicle USES communication technology to compare the communication technology of the c-v2x car, and the advantages of the communication technology of the c-v2x vehicle are present. By comparing the development of c-v2x in Europe, the United States, South Korea, Japan and China, it is a sign that China has been a late, but late, in the city of wuxi, the first urban demonstration site in the world's first real open transport environment. This paper also selects the corresponding communication ability of communication equipment in different situations, and concludes that the relevant conclusions will promote the development of the network.

A Tutorial on 5G NR V2X Communications

The Third Generation Partnership Project (3GPP) has recently published its Release 16 that includes the first Vehicle to-Everything (V2X) standard based on the 5G New Radio (NR) air interface. 5G NR V2X introduces advanced functionalities on top of the 5G NR air interface to support connected and automated driving use cases with stringent requirements. This paper presents an in-depth tutorial of the 3GPP Release 16 5G NR V2X standard for V2X communications, with a particular focus on the sidelink, since it is the most significant part of 5G NR V2X. The main part of the paper is an in-depth treatment of the key aspects of 5G NR V2X: the physical layer, the resource allocation, the quality of service management, the enhancements introduced to the Uu interface and the mobility management for V2N (Vehicle to Network) communications, as well as the co-existence mechanisms between 5G NR V2X and LTE V2X. We also review the use cases, the system architecture, and describe the evaluation methodology and simulation assumptions for 5G NR V2X. Finally, we provide an outlook on possible 5G NR V2X enhancements, including those identified within Release 17.

The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment

As the population and vehicle ownership increase, emission of pollutants is also increasing. The percentage of GHG emission by transportation sector is about 21 % in 2015 (OECD), and this may be caused by frequent stop-and-go phenomenon or delay time of vehicles in signalized intersection. Generally, these could be minimised by driving in constant speed or decreasing the delay times with an efficient traffic signal control. On the other hand, researches try to decrease vehicles’ delay time and to exclude the unnecessary stop-and-go phenomenon in an urban signalized intersection with an advent of V2X (Vehicle-to-Everything) technology development. Especially, in traditional pre-timed traffic signal control situation, even the autonomous vehicles would be impossible to exhibit their own maximum performance. Thus, the development of the traffic signal control system could have effects not only on the traffic flow but also on environmental aspects, which optimizes the signalized traffic flow based on the real-time vehicle information. In this research, on the premise of V2X environment, changes in traffic flow and the emission are analysed based on microscopic traffic information. In specific, the reinforcement learning model is constructed based on Deep Learning which learns the real-time traffic information and displays the optimal traffic signal. The performance of the system was analysed through microscopic traffic simulator - Vissim. The proposed system is expected to contribute on analysing the traffic flow and the environmental effects. Also, it is expected to contribute on constructing the green smart cities with an advent of autonomous vehicle operation in future V2X environment.

Fuzzy Traffic Control with Vehicle-to-Everything Communication.

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR.