2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)最新文献_第10页

Kalman Filtering-Aided Hybrid Indoor Positioning System With Fingerprinting And Multilateration 卡尔曼滤波辅助指纹与多重定位混合室内定位系统

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129422

Angela Cristina Eyng, O. Rayel, E. Oroski, J. L. Rebelatto

{"title":"Kalman Filtering-Aided Hybrid Indoor Positioning System With Fingerprinting And Multilateration","authors":"Angela Cristina Eyng, O. Rayel, E. Oroski, J. L. Rebelatto","doi":"10.1109/VTC2020-Spring48590.2020.9129422","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129422","url":null,"abstract":"In this work we resort to the Bluetooth Low Energy (BLE) beaconing mechanism to propose an hybrid indoor positioning system (H-IPS) that fuses both multilateration (MLT) and fingerprinting (FP) RSSI-based approaches. The aim is to estimate the localization of an indoor target node, which is assumed to follow a uniform motion model. We adopt Kalman Filtering (KF) to diminish the MLT and FP errors while performing a track-to-track fusion (TTF) of the two KF outputs to further improve the performance. Our results indicate that the proposed H-IPS improves the estimation accuracy when individually compared to the standalone FP scheme in up to 46% in the considered scenarios, while the standalone MLT is outperformed in approximately 54%. Moreover, we also provide some insights on the influence of parameters such as the FP grid size, number of access points (APs) and number of samples on the accuracy of the proposed scheme. Finally, we show that the probability that the distance error of the proposed H-IPS is lower than 2 m is 92%, while for the FP and MLT the same probability is 43% and 47%, respectively.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123074237","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: 7

Systematic Beam Management in mmWave Network: Tradeoff Among User Mobility, Link Outage, and Interference Control 毫米波网络中的系统波束管理:用户移动性、链路中断和干扰控制之间的权衡

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129387

Honghao Ju, Yan Long, X. Fang, Rong He

{"title":"Systematic Beam Management in mmWave Network: Tradeoff Among User Mobility, Link Outage, and Interference Control","authors":"Honghao Ju, Yan Long, X. Fang, Rong He","doi":"10.1109/VTC2020-Spring48590.2020.9129387","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129387","url":null,"abstract":"In this paper, we study the beam management in mmWave network from a systematic perspective. First, instead of optimizing the network performance regarding the sum-rate, we improve the beam coverage to better support user mobility, which can further reduce the beam tracking overhead. Second, to compensate for the severe signal attenuation in mmWave band, we conFigure the beam so as to satisfy the user link outage probability constraint. Third, to reduce the interference among users, we consider the inter-beam interference from both main-lobe and side-lobe. We formulate our beam management scheme as a non-linear integer optimization problem, which typically has high computational complexity. By deliberately transforming it to a geometric optimization problem and designing the rounding method, we give an optimized feasible solution with low computational complexity. We verify the performance of our beam management scheme through simulation. Extensive simulation results demonstrate that our proposed algorithm can efficiently manage the beam in mmWave network.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114221760","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: 4

EP-based Detection for Uplink OFDM-IDMA with Carrier Frequency Offsets 基于ep的载波频偏OFDM-IDMA上行链路检测

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9128720

Yun Chen, Yue Xiao

引用次数: 0

Blind SIR Estimation by Convolutional Neural Network Using Visualized IQ Constellation 基于可视化IQ星座的卷积神经网络盲SIR估计

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9128719

K. Maruta, S. Kojima, C. Ahn, D. Hisano, Yu Nakayama

引用次数: 1

Wireless Healthcare System for Life Detection and Vital Sign Monitoring 用于生命检测和生命体征监测的无线医疗系统

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9128431

Lili Xie, Jun Tian, Hongchun Li, Qian Zhao

引用次数: 4

An Access Control Mechanism Based on Risk Prediction for the IoV 基于风险预测的车联网接入控制机制

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129056

Yuanni Liu, Man Xiao, Y. Zhou, Di Zhang, Jianhui Zhang, H. Gačanin, Jianli Pan

{"title":"An Access Control Mechanism Based on Risk Prediction for the IoV","authors":"Yuanni Liu, Man Xiao, Y. Zhou, Di Zhang, Jianhui Zhang, H. Gačanin, Jianli Pan","doi":"10.1109/VTC2020-Spring48590.2020.9129056","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129056","url":null,"abstract":"The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116628739","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: 5

En-route Multilateration System Based on ADS-B and TDOA/AOA for Flight Surveillance Systems 基于ADS-B和TDOA/AOA的飞行监视航路倍增系统

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129436

Dongxu Zhao, Jinlong Sun, Guan Gui

引用次数: 8

Visible Light Indoor Positioning Algorithm Base on the Fruit Fly Modified DV-hop Method 基于果蝇改进DV-hop法的可见光室内定位算法

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129477

Yuexia Zhang, S. Yin, Jiacheng Jin

引用次数: 1

Experimental assessment of wheel-terrain interaction model suitability and applicability 车轮-地形相互作用模型适宜性和适用性的实验评价

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129547

Yuewei He, Yongkun Zhao, Zi Wang, Wenjun Zhu, Lihang Feng

引用次数: 1

Maximizing Connection Density in NB-IoT Networks with NOMA 利用NOMA实现NB-IoT网络连接密度最大化

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129542

Shashwat Mishra, Lou Salaün, Chung Shue Chen

{"title":"Maximizing Connection Density in NB-IoT Networks with NOMA","authors":"Shashwat Mishra, Lou Salaün, Chung Shue Chen","doi":"10.1109/VTC2020-Spring48590.2020.9129542","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129542","url":null,"abstract":"We address the issue of maximizing the number of connected devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The scheduling assignment is done on a per-transmit time interval (TTI) basis and focuses on efficient device clustering. We formulate the problem as a combinatorial optimization problem and solve it under interference, rate and sub-carrier availability constraints. We first present the bottom-up power filling algorithm (BU), which solves the problem given that each device can only be allocated contiguous sub-carriers. Then, we propose the item clustering heuristic (IC) which tackles the more general problem of non-contiguous allocation. The novelty of our optimization framework is two-fold. First, it allows any number of devices to be multiplexed per sub-carrier, which is based on the successive interference cancellation (SIC) capabilities of the network. Secondly, whereas most existing works only consider contiguous sub-carrier allocation, we also study the performance of allocating non-contiguous sub-carriers to each device. We show through extensive simulations that non-contiguous allocation through IC scheme can outperform BU and other existing contiguous allocation methods.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125282806","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: 8