2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)最新文献_第8页

Vehicle Localization utilizing a Novel Hybrid TDOA-Based Estimation 基于新型混合tdoa估计的车辆定位

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012963

Oscar L. Owen, Zhenni Pan, S. Shimamoto

{"title":"Vehicle Localization utilizing a Novel Hybrid TDOA-Based Estimation","authors":"Oscar L. Owen, Zhenni Pan, S. Shimamoto","doi":"10.1109/VTC2022-Fall57202.2022.10012963","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012963","url":null,"abstract":"This research investigates the use of a hybrid technique to locate vehicle positions on a 2D plane solely via other vehicles to further the future realization of Vehicle-to-Vehicle (V2V) communication. An approach in which trilateration and Time Difference Of Arrival (TDOA) are combined to estimate the Direction Of Arrival (DOA) of an incoming signal is considered. By using TDOA measurements of receivers on the Receiver Vehicle (RV), estimation regions are constructed to robustly obtain the Transmitter Vehicle (TV) position. This proposal not only creates a method for TDOA to be directly used in V2V communication but compared to other localization methods such as TOA (Time Of Arrival), the proposed technique does not need to consider time synchronization between the TV and RV, allowing for usage in a larger variety of on-road scenarios. A regression model is also implemented to further improve the accuracy of the estimation. Evaluation of the proposal is conducted for same side DOA and opposing side DOA. The DOA estimation was compared with a theoretically ideal scenario incorporating TOA. For further clarification of the methods utility and to mimic the transmission signal in road environments, the proposal is also tested in a ray tracing propagation model. The simulations show that the proposed solution accompanied with the regression model estimated the DOA in a 1 nanosecond (ns) time step environment to 1.92° accuracy and 0.08°accuracy in a 0.1ns time step environment.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115915538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost Efficient UAV Deployment and Resource Allocation for UAV-Assisted Networks 基于无人机辅助网络的高效无人机部署与资源分配

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012879

Lin He, Rong Chai, Ruijin Sun

{"title":"Cost Efficient UAV Deployment and Resource Allocation for UAV-Assisted Networks","authors":"Lin He, Rong Chai, Ruijin Sun","doi":"10.1109/VTC2022-Fall57202.2022.10012879","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012879","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In this paper, we study UAV deployment problem in an integrated access and backhaul network, where a number of UAVs are deployed as aerial base stations (ABSs) or aerial relays (ARs) to forward GUs’ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where UAV deployment, GU association and route selection problem are optimized. To solve the formulated non-convex problem, we propose a two-stage heuristic algorithm. In the first stage, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy. Given the obtained ABS deployment strategy, in the second stage, we then design a joint AR deployment, route selection scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Numerical results verify the effectiveness of the proposed algorithm.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Clustering Algorithm Based on Node Cost and Service Priority for Urban Rail In-Vehicle Ad-Hoc Network 基于节点成本和服务优先级的城市轨道车载自组织网络聚类算法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012727

Zhaoyang Su, Liu Liu, Shiyuan Cai, Lei Suo, Feng Bao

引用次数: 0

Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network 基于多尺度网络射频指纹的特定发射器识别

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013023

Yibin Zhang, Yang Peng, B. Adebisi, Guan Gui, H. Gačanin, H. Sari

{"title":"Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network","authors":"Yibin Zhang, Yang Peng, B. Adebisi, Guan Gui, H. Gačanin, H. Sari","doi":"10.1109/VTC2022-Fall57202.2022.10013023","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013023","url":null,"abstract":"The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122178824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Cybersecurity and Capacity Requirement for Data Storage of Autonomous Driving System 自动驾驶系统数据存储的网络安全和容量需求

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012699

Insup Kim, Gang-Lee Lee, Seyoung Lee, W. Choi

{"title":"Cybersecurity and Capacity Requirement for Data Storage of Autonomous Driving System","authors":"Insup Kim, Gang-Lee Lee, Seyoung Lee, W. Choi","doi":"10.1109/VTC2022-Fall57202.2022.10012699","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012699","url":null,"abstract":"Autonomous vehicles (AVs) require large dataset to perceive surrounding accurately, and continuous connectivity to update software frequently. The more connection and data the vehicle has the more cybersecurity incidents could occur. To address the challenges of AVs development, new regulations and standards have been introduced from Event Data Recorder (EDR) and Data Storage System for Automated Driving (DSSAD) to automotive cybersecurity, and these new regulations and requirements demand AVs to equip large data storage to analyze accidents of AVs. New data storage for AVs could bring new cybersecurity risks. The main purpose of this paper is to derive data storage requirements for automated driving system (ADS) and to conduct systematic cybersecurity risk analysis for data storage. In this paper, the regulations and standards for AVs are reviewed and new requirements for data storage of automated driving system are derived based on that. Plus, cybersecurity risk of the future data storage is analyzed with threat analysis and risk analysis (TARA) method. Finally, cybersecurity validation and verification methods have been researched for data storage of AVs.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124214556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Caching and Computing of Software-Defined Space-Air-Ground Integrated Networks for Video Streaming Service Improvement 面向视频流业务改进的软件定义天空地集成网络联合缓存与计算

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012719

Tianyi Zhou, C. Liang, Qianbin Chen

引用次数: 2

Cognitive Risk Control for Anti-Eavesdropping in Connected and Autonomous Vehicles Network 网联自动驾驶汽车网络反窃听认知风险控制

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012876

Yu Yao, Junhui Zhao, Zeqing Li, Xu Cheng, Lenan Wu, Xuan Li

{"title":"Cognitive Risk Control for Anti-Eavesdropping in Connected and Autonomous Vehicles Network","authors":"Yu Yao, Junhui Zhao, Zeqing Li, Xu Cheng, Lenan Wu, Xuan Li","doi":"10.1109/VTC2022-Fall57202.2022.10012876","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012876","url":null,"abstract":"Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126042758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imperfect CSI Based Design for Intelligent Reflecting Surface Assisted MISO Systems 基于不完美CSI的智能反射面辅助MISO系统设计

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013072

Hongchao Chen, Simeng Xu, Jiajia Wang, Meifang Jing, Yuhan Hu, Yi Zhao, Xiaohui Yang

引用次数: 0

Structured Phase Retrieval-aided Channel Estimation for Millimeter-Wave/Sub-Terahertz MIMO Systems 毫米波/次太赫兹MIMO系统的结构化相位检索辅助信道估计

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012988

Kaizhen Liu, Xiangning Li, Haiyang Zhao, Guoping Fan

引用次数: 1

Optimal Multicast Scheduling for Switched Beamforming Systems Leveraging Reflections 利用反射的交换波束形成系统的最优组播调度

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013079

Chao Chen, Ziye Li, S. Baek, Rui Yin, Xiaohan Yu, Chuanhuang Li

引用次数: 0