2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)最新文献

Welcome from the TPC Co-Chairs 欢迎各位TPC的共同主席

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-09-01 DOI: 10.1109/vetecs.2012.6239858

A. Conti, I. Collings, W. Chen

引用次数: 0

Beamforming in Multi-User MISO Cellular Networks with Deep Reinforcement Learning 基于深度强化学习的多用户MISO蜂窝网络波束形成

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448736

Hongchao Chen, Zhongxing Zheng, Xiaohui Liang, Yupu Liu, Yi Zhao

引用次数: 3

Elastic Queueing Engine for Time Sensitive Networking 时间敏感网络的弹性排队引擎

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448758

Angela Gonzalez Mariño, F. Fons, Ahmed Gharba, Li Ming, J. M. Aróstegui

{"title":"Elastic Queueing Engine for Time Sensitive Networking","authors":"Angela Gonzalez Mariño, F. Fons, Ahmed Gharba, Li Ming, J. M. Aróstegui","doi":"10.1109/VTC2021-Spring51267.2021.9448758","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448758","url":null,"abstract":"In this work we introduce the concept of Elastic Queueing Engine (EQE) for Time Sensitive Networking (TSN) which is a new networking-optimized queueing strategy designed to maximize Quality of Service (QoS) and usability of resources in time sensitive networks. The proposed dynamic run-time adaptation of queues to network status exploits usability of resources by introducing three new degrees of elasticity in Hardware (HW), i.e., the system is able to dynamically adapt to events in incoming traffic by optimizing the usage of available resources. The three new degrees of elasticity introduced by EQE are: dynamic queues size, dynamic Internal Priority Value (IPV) management and dynamic Gate Control List (GCL). Apart from inventing the queueing closed-loop control algorithms, EQE synthesizes these three functionalities in HW as co-processors attached to the system CPU to guarantee a real-time response to ingress frames, aimed at contributing towards fail-safe and fail-operational In-Vehicle Network (IVN) solutions. Our results show that as long as there is available memory in the system, EQE for TSN can completely avoid frame drops in scenarios where static queues systems would be compromised, while other network KPIs (maximum latency, cost) remain similar to implementations without the proposed new features.","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":"125825862","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

Energy Detection for M-QAM Signals M-QAM信号的能量检测

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448730

Shun Ishihara, K. Umebayashi, Janne J. Lehtomäki

{"title":"Energy Detection for M-QAM Signals","authors":"Shun Ishihara, K. Umebayashi, Janne J. Lehtomäki","doi":"10.1109/VTC2021-Spring51267.2021.9448730","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448730","url":null,"abstract":"In this paper, we address energy detection for M-ary quadrature amplitude modulation (QAM) signals. In the literature deterministic signal model is widely used and detection probability is a function of signal energy. Unlike constant amplitude signals, the QAM signal is not deterministic since the energy in each QAM symbol can randomly vary. For random signals, model where both signal and noise are Gaussian has been widely used. However, this approximation may not provide accurate detection probability for QAM signals. Instead the detection probability should be averaged over the distribution of the energy. Previous work has considered calculating exact detection probability for given M analytically. However, the method presented previously has complexity that increases as a function of M and the number of samples.In this paper, we show that the distribution of observed energy for any M can be accurately approximated by one distribution which is derived analytically. Multiple numerical results showing probability density function, Kolmogorov-Smirnov distance, and detection probability are shown. Based on these results, a range where the proposed approximation is applicable is obtained.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"34 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":"126102952","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

Mobility-Aware QoS Promotion and Load Balancing in MEC-Based Vehicular Networks: A Deep Learning Approach 基于mec的车载网络移动感知QoS提升与负载均衡:一种深度学习方法

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448705

Chih-Ho Hsu, Yao Chiang, Yi Zhang, Hung-Yu Wei

{"title":"Mobility-Aware QoS Promotion and Load Balancing in MEC-Based Vehicular Networks: A Deep Learning Approach","authors":"Chih-Ho Hsu, Yao Chiang, Yi Zhang, Hung-Yu Wei","doi":"10.1109/VTC2021-Spring51267.2021.9448705","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448705","url":null,"abstract":"Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"66 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":"123600490","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

Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks 基于LSTM递归神经网络的车辆轨迹预测

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449038

A. Ip, Luis Irio, Rodolfo Oliveira

引用次数: 12

Fraud-resilient Privacy-preserving Crowd-sensing for Dynamic Spectrum Access 动态频谱接入的抗欺诈保护隐私人群感知

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448648

Erald Troja, Ice Lin

引用次数: 0

iVRLS: In-coverage Vehicular Reinforcement Learning Scheduler iVRLS:覆盖车辆强化学习调度

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448993

T. Şahin, Mate Boban, R. Khalili, A. Wolisz

{"title":"iVRLS: In-coverage Vehicular Reinforcement Learning Scheduler","authors":"T. Şahin, Mate Boban, R. Khalili, A. Wolisz","doi":"10.1109/VTC2021-Spring51267.2021.9448993","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448993","url":null,"abstract":"Cellular networks enable high reliability of vehicle-to-vehicle (V2V) communications thanks to centralized, efficient coordination of radio resources. Collision-free transmissions are possible, where base stations could allocate orthogonal resources to the vehicles. However, in case of limited resources in relation to the data traffic load, the resource allocation task becomes a challenge. Current solutions propose heuristic algorithms that focus on resource reuse, often based on the location of the vehicles. Such schedulers are mainly designed assuming ideal network coverage conditions and are prone to performance degradation in case of coverage loss. Further, they typically rely on frequent scheduling updates, which increases the dependency on coverage. In this paper, we propose a reinforcement learning-based approach to scheduling V2V communications. Our solution, called iVRLS, delivers higher reliability than an enhanced version of a state-of-the-art benchmark algorithm in case of intermittent coverage conditions, while requiring less frequent scheduling. Following this approach, we enable a unified scheduler deployment irrespective of coverage, which offers graceful performance behavior across varying coverage conditions, thus making iVRLS a robust alternative to existing schedulers.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"60 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":"126639455","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: 2

Adaptive Fuzzy Tuning Framework for Autonomous Vehicles: An Experimental Case Study 自动驾驶汽车的自适应模糊调谐框架:一个实验案例研究

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448666

S. Samokhin, M. Mehndiratta, U. Hamid, J. Saarinen

引用次数: 0

Collective Perception service for Connected Vehicles and Roadside Infrastructure 联网车辆与路边基础设施的集体感知服务

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448753

Ameni Chtourou, Pierre Merdrignac, O. Shagdar

{"title":"Collective Perception service for Connected Vehicles and Roadside Infrastructure","authors":"Ameni Chtourou, Pierre Merdrignac, O. Shagdar","doi":"10.1109/VTC2021-Spring51267.2021.9448753","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448753","url":null,"abstract":"Collective Perception Service (CPS) is under standardisation at European Telecommunications Standardisation Institute (ETSI) with the objective of enabling sensor-equipped vehicles and roadside units (RSU) to notify nearby vehicles of the objects detected by their sensors. This service is expected to significantly improve perception of vehicles and, hence, improve road safety. While both vehicles and RSUs can contribute in CPS, they have very different characteristics in terms of mobility, sensor field of view (FOV), communication coverage, processing capacity, and service integration cost. This paper compares CPS provided by vehicles and by RSUs, particularly their impacts on extending vehicles’ perception. We will first develop an analytical model that formulates the number of objects perceived by vehicles and RSUs and the success probability of CPM targeting the IEEE 802.11p technology. The analytical results will then be confirmed by simulation evaluations conducted using the VEINS simulator. The analytical and simulation results reveal that, RSU-assisted CPS significantly outperforms vehicle-assisted CPS, providing up to 8-times higher effective number of perceived objects. The results suggest a great opportunity of optimising radio resource utilisation without degrading collective perception by exploiting roadside infrastructure.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"5 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":"115353701","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: 12