2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)最新文献_第2页

V2V Test Scenario-Study on Intersection Collision Warning 交叉口碰撞预警V2V测试场景研究

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448757

Yaqi Xu, Yuming Ge, Da Wei, Rundong Yu, Jian Wang, Zhihan Yao

引用次数: 1

On the Design of Quantization Functions for Uplink Massive MIMO with Low-Resolution ADCs 基于低分辨率adc的上行海量MIMO量化功能设计

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448919

Lifu Liu, Songyan Xue, Yi Ma, N. Yi, R. Tafazolli

引用次数: 3

An Efficient ToA Estimation Technique Based on Phase Correction for 5G mMTC system 基于相位校正的5G mMTC系统ToA估计方法

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448888

Xining Yang, Jinhong Yuan, Yiqing Zhou, Jinglin Shi

引用次数: 0

Video Quality and Latency for UAV Teleoperation over LTE: A Study with ns3 基于ns3的无人机LTE远程操作视频质量与时延研究

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448676

Antonia Stornig, Aymen Fakhreddine, H. Hellwagner, P. Popovski, C. Bettstetter

引用次数: 9

WiFi CSI-Based Device-free Multi-room Presence Detection using Conditional Recurrent Network 基于WiFi csi的条件循环网络无设备多房间存在检测

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448848

Fang-Yu Chu, Chun-Jie Chiu, An-Hung Hsiao, Kai-Ten Feng, Po-Hsuan Tseng

{"title":"WiFi CSI-Based Device-free Multi-room Presence Detection using Conditional Recurrent Network","authors":"Fang-Yu Chu, Chun-Jie Chiu, An-Hung Hsiao, Kai-Ten Feng, Po-Hsuan Tseng","doi":"10.1109/VTC2021-Spring51267.2021.9448848","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448848","url":null,"abstract":"Human presence detection via camera-based monitoring systems has been well-adopted in various applications including smart homes, factories, and hospitals. However, its privacy concerns have been raised in many occasions such as daycare centers and homes with elderly living alone. In recent years, literatures adopting wireless signals were proposed to resolve privacy issues for presence detection; nevertheless, existing works can only be applied in a single room scenario. In this paper, we are the first work to propose a device-free multi-room human presence detection system based on efficient star network topology. Our proposed conditional recurrent architecture-based multi-room presence detection (C-MuRP) system extracts both spatial and temporal features from the Wi-Fi channel state information (CSI). Associated with a voting scheme, the proposed novel deep-learning architecture classifies the states of multi-room with the condition on current waveform to emphasize present feature states. Real-time experimental results showed that our proposed C-MuRP system can achieve higher accuracy for multi-room presence detection compared to existing methods.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791225","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: 14

Decentralized Planning-Assisted Deep Reinforcement Learning for Collision and Obstacle Avoidance in UAV Networks 分布式规划辅助深度强化学习在无人机网络中的碰撞与避障

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448710

Juntong Lin, Hsiao-Ting Chiu, Rung-Hung Gau

引用次数: 3

The Implementation of a SIP-Based Service Platform for 5G IoT Applications 基于sip的5G物联网应用业务平台的实现

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448772

I-Fen Yang, Yi-Chun Lin, Shun-Ren Yang, P. Lin

{"title":"The Implementation of a SIP-Based Service Platform for 5G IoT Applications","authors":"I-Fen Yang, Yi-Chun Lin, Shun-Ren Yang, P. Lin","doi":"10.1109/VTC2021-Spring51267.2021.9448772","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448772","url":null,"abstract":"Internet of things (IoT) technologies have been applied to realize various applications/services, ranging from massive, broadband, critical to industrial automation IoT applications. Recently, several IoT service platforms have been proposed to facilitate IoT application deployment, such as OpenMTC and IoTtalk. These platforms typically utilize the lightweight MQTT or CoAP application protocol to communicate with their served devices, both optimized for massive IoT applications within constrained networks. Unfortunately, these protocols are not applicable to those more advanced IoT applications, which demand high data rate and low latency. To provide a full range of supports for different IoT application scenarios, Session Initiation Protocol (SIP) can be a better candidate, which, besides instant messaging, can also handle the long session semantic and the publish-subscribe semantic. In the literature, different SIP semantics have been utilized to implement different IoT applications/systems. However, to the best of our knowledge, there exists no generic SIP-based IoT service platform that integrates relevant capabilities for developers to flexibly develop/deploy heterogeneous IoT applications, demanding different quality of service. This paper implements a SIP-based IoT service platform, iSIPtalk, which enables rapid development of more advanced IoT applications. To justify the applicability of iSIPtalk, we deploy a real testbed and implement a vehicular service within our proposed iSIPtalk. Finally, via delay measurements using the real testbed and the vehicular service, we demonstrate the performance of our iSIPtalk.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124239331","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: 3

A Two-Timescale Resource Allocation Scheme in Vehicular Network Slicing 车辆网络切片中的双时间尺度资源分配方案

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448852

Yaping Cui, Xinyun Huang, P. He, D. Wu, Ruyang Wang

{"title":"A Two-Timescale Resource Allocation Scheme in Vehicular Network Slicing","authors":"Yaping Cui, Xinyun Huang, P. He, D. Wu, Ruyang Wang","doi":"10.1109/VTC2021-Spring51267.2021.9448852","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448852","url":null,"abstract":"Network slicing can support the diverse use cases with heterogeneous requirements, and has been considered as one of the key roles in future networks. However, as the dynamic traffic demands and the mobility in vehicular networks, how to perform RAN slicing efficiently to provide stable quality of service (QoS) for connected vehicles is still a challenge. In order to meet the diversified service request of vehicles in such a dynamic vehicular environment, in this paper, we propose a two-timescale radio resource allocation scheme, namely, LSTM-DDPG, to provide stable service for vehicles. Specifically, for the long-term dynamic characteristics of service request from vehicles, we use long short-term memory (LSTM) to follow the tracks, such that the dedicated resource allocation is executed in a long timescale by using historical data. On the other hand, for the impacts of channel changes caused by high-speed movement in a short period, a deep reinforcement learning (DRL) algorithm, i.e., deep deterministic policy gradient (DDPG), is leveraged to adjust the allocated resources. We prove the effectiveness of the proposed LSTM-DDPG with simulation results, the cumulative probability that the slice supplies a stable performance to the served vehicle within the resource scheduling interval can reach more than 90%. Compared with the conventional deep Q-networks (DQN), the average cumulative probability has increased by 27.8%.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299840","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: 3

Travel Time Prediction and Route Performance Analysis in BRTS based on Sparse GPS Data 基于GPS稀疏数据的BRTS行程时间预测与路由性能分析

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448832

A. Kakarla, V. K. Munagala, T. Ishizaka, A. Fukuda, S. Jana

{"title":"Travel Time Prediction and Route Performance Analysis in BRTS based on Sparse GPS Data","authors":"A. Kakarla, V. K. Munagala, T. Ishizaka, A. Fukuda, S. Jana","doi":"10.1109/VTC2021-Spring51267.2021.9448832","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448832","url":null,"abstract":"A Bus Rapid Transit System (BRTS) with earmarked lanes potentially provides efficient public transportation, and helps in controlling urban traffic congestion. While travel time prediction (TTP) is essential in a BRTS, existing algorithms generally assume GPS logs available at short uniform intervals. However, those are rarely evaluated on BRTS in emerging economies, where logged GPS data could be available at sparse nonuniform intervals. To fill the gap, we study the efficacy of certain well known ML models, namely, Random Forests (RF), Light Gradient Boosting (LGB), and Extreme Gradient Boosting (XGBoost, XGB) in utilizing historical data. Performance of those ensemble learning methods is compared with that of a conventional travel time prediction (CTTP) method, which uses historical averaging. It was found that XGB was superior to other methods at hand, and the prediction error by approximately 60% compared to the CTTP method. Alongside improving the experience of commuters, the proposed XGB-based TTP method also improves the estimation of intersection crossing time (ICT), which potentially leads to efficient traffic policy making.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116343695","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

Design aspects on physical layer structure for 5G V2X and related issues 5G V2X物理层结构设计及相关问题

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448874

Mengmeng Liu, Haoyu Deng, Zhenting Li, Yongdong Zhu

引用次数: 0