2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)最新文献_第3页

Joint Client Association and UAV Scheduling in Cache-Enabled UAV-Assisted Vehicular Networks 基于缓存的无人机辅助车辆网络中的联合客户关联和无人机调度

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448868

Shichao Zhu, Lin Gui, Qi Zhang, Xiupu Lang

{"title":"Joint Client Association and UAV Scheduling in Cache-Enabled UAV-Assisted Vehicular Networks","authors":"Shichao Zhu, Lin Gui, Qi Zhang, Xiupu Lang","doi":"10.1109/VTC2021-Spring51267.2021.9448868","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448868","url":null,"abstract":"In the case of explosive content requests being generated by numerous vehicles in rush hour, the cellular downlink resources become insufficient. This paper considers two supplementary schemes for exploiting the uplink resources: One is letting vehicles cache the browsed contents so that the vehicles can share contents with one another; and the other is dispatching a cache-enabled UAV to transmit contents to the vehicles nearby. We formulate a joint optimization problem for maximizing the average data rate of vehicles, and then decompose it into the subproblems of client association and UAV scheduling. The client association aims at matching the vehicles to the resources, and the UAV scheduling is to update the UAV’s caching and trajectory to adapt to the environment changing. The two subproblems focus respectively on the current condition and the long term profit, and are solved respectively by a matching-based algorithm and a deep reinforcement learning-based algorithm. Our simulation results based on a real-world traffic data set demonstrate the advantages of the proposed approaches.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"33 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":"124380412","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

Efficient Power Allocation for Cognitive Radio NOMA using Game-Theoretic Based Pricing Strategy 基于博弈论定价策略的认知无线电NOMA有效功率分配

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448851

Shaima' S. Abidrabbu, H. Arslan

引用次数: 4

System-Level Analysis of mmWave 5G Systems with Different Multi-Panel Antenna Device Models 不同多面板天线器件型号毫米波5G系统的系统级分析

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449044

Fuad Mousse Abinader Jr., C. Rom, K. Pedersen, Sofonias Hailu, Niko Kolehmainen

引用次数: 12

Near-field localization using machine learning: an empirical study 使用机器学习的近场定位:一项实证研究

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449002

M. Laakso, R. Wichman

引用次数: 0

A Task Assignment Scheme for Parked-Vehicle Assisted Edge Computing in IoV 车联网中停车辅助边缘计算的任务分配方案

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448735

Qingxia Peng, Yunjian Jia, Liang Liang, Zhengchuan Chen

{"title":"A Task Assignment Scheme for Parked-Vehicle Assisted Edge Computing in IoV","authors":"Qingxia Peng, Yunjian Jia, Liang Liang, Zhengchuan Chen","doi":"10.1109/VTC2021-Spring51267.2021.9448735","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448735","url":null,"abstract":"Vehicular edge computing (VEC) has been envisioned as an important application of edge computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VEC. They cooperate with edge severs to process offloading tasks at the vehicular network edge, leading to a new paradigm called parked-vehicle assisted edge computing (PVEC) in the Internet of Vehicles (IoV). However, recent researchers mostly focus on how to optimize the total cost of requesting vehicle (RV), and rarely pay attention to the optimization of the utility of PVs that provide services, including the reward from RV and the overhead of executing task. In this paper, we study a task assignment problem with computing delay constraints for PVEC in IoV. Specially, extra performance loss caused by offloading subtasks to PVs is taken into the cost function of RV. The optimal task assignment problem is formulated and solved with the Stackelberg game framework and a ternary search-based algorithm to minimize the cost of RV and maximize the utility of PVs. Finally, extensive numerical results are provided to demonstrate that our scheme is more efficient in deducing the total cost of RV and increasing the reward for PVs than other two existing schemes.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"75 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":"115782313","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

QoS Optimization for Distributed Edge Computing System: A Multi-agent State-based Learning Approach 分布式边缘计算系统的QoS优化:一种基于多智能体状态的学习方法

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449000

Fenghui Zhang, M. Wang, Liqing Shan, Xiangqing Wang, Maosheng Fu, Xiancun Zhou

{"title":"QoS Optimization for Distributed Edge Computing System: A Multi-agent State-based Learning Approach","authors":"Fenghui Zhang, M. Wang, Liqing Shan, Xiangqing Wang, Maosheng Fu, Xiancun Zhou","doi":"10.1109/VTC2021-Spring51267.2021.9449000","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9449000","url":null,"abstract":"Placement of edge computing servers at the edge of the network can reduce task transmission delay. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the number of tasks offloaded to each edge server may be quite different, which will seriously affect the QoS of the system. To this end, we investigate the QoS improvement of the distributed edge computing system from the game-theoretic perspective and propose a multi-agent state-based learning algorithm. Firstly, by modeling the cost of an edge computing server as the deviation between its execution time and the system average execution time, we formulate the QoS improvement of the system as a state-based game where each agent competes to maximize its own utility. Then, we propose a multi-agent state-based learning algorithm to obtain the pure Nash equilibrium strategy of each agent. Finally, compared with the existing approaches, the experiments show that the proposed algorithm can improve the QoS of the distributed edge computing system.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"131 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":"115879794","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

Location-Free Beam Prediction in mmWave Systems 毫米波系统中的无位置波束预测

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448938

Tushara Ponnada, H. Al-Tous, O. Tirkkonen

引用次数: 4

Energy Efficiency Gains for Wireless Communication Systems Aided by Ambient Backscatter 环境反向散射辅助下无线通信系统的能源效率提高

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448658

Ying Guo, Gongpu Wang, Guoqing Li, Minzheng Jia, B. Ai

引用次数: 2

Beam Management Based Multi-cell Interference Suppression for Millimeter Wave Communications 基于波束管理的毫米波通信多小区干扰抑制

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448763

Yaxin Song, Shaoyi Xu

引用次数: 1

Simultaneous Wireless Power Transfer and Modulation Classification 同步无线功率传输与调制分类

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448896

Rahul Gupta, I. Krikidis

{"title":"Simultaneous Wireless Power Transfer and Modulation Classification","authors":"Rahul Gupta, I. Krikidis","doi":"10.1109/VTC2021-Spring51267.2021.9448896","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448896","url":null,"abstract":"This work proposes a new simultaneous wireless power transfer and modulation classification (SWPTMC) scheme appropriate for internet of things (IoT) applications. The problem of SWPTMC is investigated for various modulation formats, i.e., quadrature phase-shift-keying (QPSK), π/4-QPSK, offset QPSK (OQPSK), 16-pulse amplitude modulation (16-PAM), 16-quadrature amplitude modulation (16-QAM), and minimum shift keying (MSK). We propose three receiver architectures, i.e., an integrated receiver, a separate receiver with power splitting (PS), and a separate receiver with energy harvesting (EH)-based classification; all the architectures are studied under a non-linear model with a certain sensitivity and saturation level. Also, we derive the average harvested power over a Rayleigh fading channel for the different modulation formats. Two different approaches are used for the blind modulation classification (MC) algorithm: one for the intermediate frequency signal and the other for the baseband signal. Both the MC algorithms are based on the higher-order cumulants and cyclic cumulants of the received signal. The cyclic cumulants use the non-zero cycle frequency position, while the cumulants use threshold values for classifying modulation formats. Monte Carlo simulations are used to evaluate the performance of the proposed SWPTMC schemes. The results show that we can simultaneously harvest power without much affecting the classifier performance. Moreover, with an integrated receiver, we can simultaneously perform MC and harvest power without the requirement of PS circuit.","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":"131003764","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