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Vehicle-to-Everything (V2X) Communication for Intelligent Transportation: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 4397

Special Issue Editors

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: intelligent transportation systems; internet of vehicles; distributed computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Communication Engineering, Xidian University, Xi’an 710071, China
Interests: trusted computing network; internet of things and edge computing security; wireless network physical layer security; blockchain technology; distributed collaborative attack and defense technology; data security and privacy protection
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: mobile edge computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, as a result of advanced communication technologies, V2X (vehicle-to-everything) communication has been applied to intelligent transportation systems (ITS), such as road safety, cooperative autonomous driving, entertainment services, and many other use cases. A V2X-enabled ITS guarantees more efficient and reliable travel. Increasingly, the substantial development of wireless communication technology for V2X communication and networking enables the development of novel ITS services and applications:

  • New wireless communications and networking architecture for V2X and ITS;
  • Novel theory, technology, methodology, tools, and applications for V2X and ITS;
  • Modelling, simulation, and field evaluation for V2X and ITS;
  • Big data and data analytics for V2X and ITS;
  • Machine learning techniques for V2X and ITS;
  • Edge architecture, service, and applications for V2X and ITS;
  • New paradigms and management for smart mobility;
  • Vehicular networking, vehicular cloud, and internet of vehicles (IoV);
  • Cooperative perception strategies within V2X;
  • Cooperative decision-making processes enhanced by V2X;
  • Collaborative planning mechanisms through V2X.

This Special Issue of the Sensors aims to discuss a novel design of V2X architecture, techniques, networks, services, and applications for ITS and the search for innovative solutions for meeting the expectation of V2X communication and ITS.

Prof. Dr. Chen Chen
Prof. Dr. Kai Liu
Dr. Lei Liu
Prof. Dr. Qingqi Pei
Dr. Dapeng Lan
Guest Editors

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Keywords

  • V2X (Vehicle-to-Everything)
  • intelligent transportation systems (ITS)
  • wireless communication

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 2124 KiB  
Article
Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems
by Yong Ding, Honggao Deng, Yuelei Xie, Haitao Wang and Shaoshuai Sun
Sensors 2024, 24(11), 3581; https://doi.org/10.3390/s24113581 - 1 Jun 2024
Cited by 1 | Viewed by 945
Abstract
For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose [...] Read more.
For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels. Full article
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21 pages, 1038 KiB  
Article
Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems
by Jiho Song and Seong-Hwan Hyun
Sensors 2024, 24(7), 2351; https://doi.org/10.3390/s24072351 - 7 Apr 2024
Cited by 2 | Viewed by 1477
Abstract
We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a [...] Read more.
We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance. Full article
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17 pages, 1263 KiB  
Article
Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications
by Jihun Lee, Hun Kim and Jaewoo So
Sensors 2024, 24(3), 837; https://doi.org/10.3390/s24030837 - 27 Jan 2024
Cited by 1 | Viewed by 1422
Abstract
The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, [...] Read more.
The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio. Full article
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