IEEE Open Journal of Vehicular Technology最新文献_第10页

3GPP-Compliant Datasets for xG Location-Aware Networks 符合 3GPP 标准的 xG 位置感知网络数据集

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-12-08 DOI: 10.1109/OJVT.2023.3340993

Andrea Conti;Gianluca Torsoli;Carlos A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;Moe Z. Win

{"title":"3GPP-Compliant Datasets for xG Location-Aware Networks","authors":"Andrea Conti;Gianluca Torsoli;Carlos A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;Moe Z. Win","doi":"10.1109/OJVT.2023.3340993","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3340993","url":null,"abstract":"Location awareness is vital in next generation (xG) wireless networks to enable different use cases, including location-based services (LBSs) and efficient network management. However, achieving the service level requirements specified by the 3rd Generation Partnership Project (3GPP) is challenging. This calls for new localization algorithms as well as for 3GPP-standardized scenarios to support their systematic development and testing. In this context, the availability of public datasets with 3GPP-compliant configurations is essential to advance the evolution of xG networks. This paper introduces xG-Loc, the first open dataset for localization algorithms and services fully compliant with 3GPP technical reports and specifications. xG-Loc includes received localization signals, measurements, and analytics for different network and signal configurations in indoor and outdoor scenarios with center frequencies from micro-waves in frequency range 1 (FR1) to millimeter-waves in frequency range 2 (FR2). Position estimates obtained via soft information-based localization and wireless channel quality indicators via blockage intelligence are also provided. The rich set of data provided by xG-Loc enables the characterization of localization algorithms and services under common 3GPP-standardized scenarios in xG networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10349917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140339974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Empirical Study on Channel Reciprocity in TDD and FDD Systems TDD 和 FDD 系统中信道互易性的实证研究

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-12-06 DOI: 10.1109/OJVT.2023.3339799

Huixin Xu;Jianhua Zhang;Pan Tang;Lei Tian;Qixing Wang;Guangyi Liu

{"title":"An Empirical Study on Channel Reciprocity in TDD and FDD Systems","authors":"Huixin Xu;Jianhua Zhang;Pan Tang;Lei Tian;Qixing Wang;Guangyi Liu","doi":"10.1109/OJVT.2023.3339799","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3339799","url":null,"abstract":"The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band; A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity; Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval; Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345764","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions 全面评述自动驾驶的局限性及其对事故和碰撞的影响

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-29 DOI: 10.1109/OJVT.2023.3335180

Amit Chougule;Vinay Chamola;Aishwarya Sam;Fei Richard Yu;Biplab Sikdar

{"title":"A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions","authors":"Amit Chougule;Vinay Chamola;Aishwarya Sam;Fei Richard Yu;Biplab Sikdar","doi":"10.1109/OJVT.2023.3335180","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3335180","url":null,"abstract":"The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, self-driving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Based Beam Management in 3GPP: Optimizing Data Collection Time Window for Temporal Beam Prediction 3GPP 中基于人工智能的波束管理：优化数据采集时间窗口以实现时域波束预测

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-29 DOI: 10.1109/OJVT.2023.3337357

Yingshuang Bai;Jiawei Zhang;Chen Sun;Le Zhao;Haojin Li;Xiaoxue Wang

{"title":"AI-Based Beam Management in 3GPP: Optimizing Data Collection Time Window for Temporal Beam Prediction","authors":"Yingshuang Bai;Jiawei Zhang;Chen Sun;Le Zhao;Haojin Li;Xiaoxue Wang","doi":"10.1109/OJVT.2023.3337357","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3337357","url":null,"abstract":"Artificial Intelligence (AI) has gained significant attention and extensive research across various fields in recent years. In the realm of wireless communication, researchers are exploring the use of AI to facilitate various physical layer (PHY) procedures. Within the standardization efforts of the Third Generation Partnership Project (3GPP), one prominent direction being explored is AI-based beam management (BM). The primary objective is to harness AI techniques for predicting optimal beams, thereby reducing measurement overhead and latency. This paper aims to discuss the progress made in AI-based beam management within the Release 18 standardization. Furthermore, through our research, we have identified the mobile speed of user equipment (UE) as a crucial factor that impacts the optimal time window size for collecting input data in AI models. We have observed an inverse correlation between UE speed and the time window size. Accordingly, to mitigate unnecessary measurement overhead and latency, we propose that the determination of the time window size for input data collection should be based on the UE speed. Additionally, we will present our simulation results and provide a comprehensive analysis of this relationship.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software-Defined Radio-Based IEEE 802.15.4 SUN OFDM Evaluation Platform for Highly Mobile Environments 基于软件定义无线电的 IEEE 802.15.4 SUN OFDM 评估平台，适用于高度移动环境

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-28 DOI: 10.1109/OJVT.2023.3337315

Keito Nakura;Shota Mori;Hiroko Masaki;Hiroshi Harada

{"title":"Software-Defined Radio-Based IEEE 802.15.4 SUN OFDM Evaluation Platform for Highly Mobile Environments","authors":"Keito Nakura;Shota Mori;Hiroko Masaki;Hiroshi Harada","doi":"10.1109/OJVT.2023.3337315","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3337315","url":null,"abstract":"Next-generation Internet of Things (IoT) systems require faster data transmission, support for moving objects, and long-distance transmission when compared to the currently available IoT systems. The IEEE 802.15.4 smart utility network (SUN) orthogonal frequency-division multiplexing (OFDM) can satisfy these requirements. Mobile-communication-oriented receiver systems are typically used in urban environments for SUN OFDM. However, the evaluation depends on computer simulations and requires an experimental evaluation platform based on software-defined radio (SDR) that can modify transmitter-receiver functions. We present a platform for SUN OFDM that enables high-speed mobile communication. The proposed platform comprises a signal generator-based transmitter and an SDR-based receiver; the receiver baseband signal processing is performed by MATLAB. We also proposed signal processing functions that can receive the SUN OFDM packets even at speeds of tens of km/h. We applied a simplified universal time-domain windowed (UTW)-OFDM scheme to this platform to operate even at sub-1 GHz, where the spectrum mask is more limited. In the experimental evaluation, the required packet error rate for SUN OFDM was achieved in an 80 km/h multipath fading environment, and out-of-band emission can be suppressed by over 43 dB from the peak power while achieving performance equivalent to that without applying the simplified UTW.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10329434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capacity Analysis of UAV-to-Ground Channels With Shadowing: Power Adaptation Schemes and Effective Capacity 带阴影的无人机对地信道的容量分析：功率自适应方案和有效容量

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-27 DOI: 10.1109/OJVT.2023.3336619

Remon Polus;Claude D'Amours

引用次数: 0

Source Separation in Joint Communication and Radar Systems Based on Unsupervised Variational Autoencoder 基于无监督变异自动编码器的联合通信和雷达系统中的信号源分离

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-22 DOI: 10.1109/OJVT.2023.3335358

Khaled A. Alaghbari;Heng Siong Lim;Benzhou Jin;Yutong Shen

{"title":"Source Separation in Joint Communication and Radar Systems Based on Unsupervised Variational Autoencoder","authors":"Khaled A. Alaghbari;Heng Siong Lim;Benzhou Jin;Yutong Shen","doi":"10.1109/OJVT.2023.3335358","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3335358","url":null,"abstract":"Source separation of a mixed signal in the time-frequency domain is critical for joint communication and radar (JCR) systems to achieve the required performance, especially at a low signal-to-noise ratio (SNR). In this paper, we propose the use of a generative model, such as the unsupervised variational autoencoder (VAE), to separate sensing and data communication signals. We first analyse the VAE system using different mask techniques; then, the best technique is selected for comparison with popular blind source separation (BSS) algorithms. We verify the performance of the proposed VAE by adopting different metrics such as the signal-to-distortion ratio (SDR), source-to-interference ratio (SIR), and sources-to-artifacts ratio (SAR). Simulation results show that the proposed VAE outperforms the BSS techniques at low SNR for the case of a mixed signal in the time-frequency domain and at low and high SNR for a mixed signal in the time domain. It enables the JCR system in the challenging first scenario to obtain SDR gains of 11.1 dB and 6 dB at 0 dB SNR for recovering the sensing and data communication signals respectively. Finally, we analyse the robustness of the JCR system in detecting an interference signal operating in the same frequency band, where the simulation result indicates an accuracy of 91% based on the proposed steps.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reduced Complexity Learning-Assisted Joint Channel Estimation and Detection of Compressed Sensing-Aided Multi-Dimensional Index Modulation 压缩传感辅助多维索引调制的低复杂度学习辅助联合信道估计与检测

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-20 DOI: 10.1109/OJVT.2023.3334822

Xinyu Feng;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo

{"title":"Reduced Complexity Learning-Assisted Joint Channel Estimation and Detection of Compressed Sensing-Aided Multi-Dimensional Index Modulation","authors":"Xinyu Feng;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2023.3334822","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3334822","url":null,"abstract":"Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multi-dimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles URANUS：无人驾驶飞行器的无线电频率跟踪、分类和识别

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-17 DOI: 10.1109/OJVT.2023.3333676

DOMENICO LOFÙ;Pietro Di Gennaro;Pietro Tedeschi;Tommaso Di Noia;Eugenio Di Sciascio

{"title":"URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles","authors":"DOMENICO LOFÙ;Pietro Di Gennaro;Pietro Tedeschi;Tommaso Di Noia;Eugenio Di Sciascio","doi":"10.1109/OJVT.2023.3333676","DOIUrl":"https://doi.org/10.1109/OJVT.2023.3333676","url":null,"abstract":"Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with 90% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE \u0000<inline-formula><tex-math>$approx 0.29$</tex-math></inline-formula>\u0000, MAE \u0000<inline-formula><tex-math>$approx 0.04$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$R^{2}approx 0.93$</tex-math></inline-formula>\u0000. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138491044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions 基于机器学习的全双工无线电自干扰消除：方法、公开挑战和未来研究方向

IF 6.4

IEEE Open Journal of Vehicular Technology Pub Date : 2023-11-09 DOI: 10.1109/OJVT.2023.3331185

Mohamed Elsayed;Ahmad A. Aziz El-Banna;Octavia A. Dobre;Wan Yi Shiu;Peiwei Wang

{"title":"Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions","authors":"Mohamed Elsayed;Ahmad A. Aziz El-Banna;Octavia A. Dobre;Wan Yi Shiu;Peiwei Wang","doi":"10.1109/OJVT.2023.3331185","DOIUrl":"10.1109/OJVT.2023.3331185","url":null,"abstract":"In contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant potential, FD systems suffer from an inherent self-interference (SI) due to a coupling of the transmit signal to its own FD receive chain. Self-interference cancellation (SIC) techniques are the key enablers for realizing the FD operation, and they could be implemented in the propagation, analog, and/or digital domains. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be insufficient to seize the growing complexity of forthcoming communication systems. For the time being, machine learning (ML) data-driven approaches have been introduced for digital SIC to overcome the complexity hurdles of traditional methods. This article reviews and summarizes the recent advances in applying ML to SIC in FD systems. Further, it analyzes the performance of various ML approaches using different performance metrics, such as the achieved SIC, training overhead, memory storage, and computational complexity. Finally, this article discusses the challenges of applying ML-based techniques to SIC, highlights their potential solutions, and provides a guide for future research directions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10314438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0