2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)最新文献_第7页

Data-Driven Multi-armed Beam Tracking for Mobile Millimeter-Wave Communication Systems 移动毫米波通信系统的数据驱动多臂波束跟踪

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012984

Shenmin Zhang, Yuan Ma, Xingjian Zhang, Jian Wang

{"title":"Data-Driven Multi-armed Beam Tracking for Mobile Millimeter-Wave Communication Systems","authors":"Shenmin Zhang, Yuan Ma, Xingjian Zhang, Jian Wang","doi":"10.1109/VTC2022-Fall57202.2022.10012984","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012984","url":null,"abstract":"The goal of the next-generation mobile communication system is higher data-rates, lower latency, and higher energy-efficient performance, which bring about the demands for fast beam tracking in time-varying mobile communication. With the development of large-scale antenna array technology, highly directional beams can be formed with limited radio frequency chains. However, traditional exhaustive searching scheme has unacceptable overhead that leads to great challenges for applying to mobile millimeter-wave environments. Fast beam tracking scheme therefore has been recognized as a key technology in millimeter wave communication. To address this issue, this paper proposes a data-driven multi-armed beam tracking scheme to select the beamforming/combining vectors that achieve the target quality of service based on the real-time measurement, rather than the prior knowledge such as channel and user mobility information in beamforming design. To further speed up the beam tracking process, multi-armed beam is created to sample multiple spatial directions simultaneously. Simulation results show that the proposed data-driven multi-armed beam tracking method could achieve fast beam tracking performance with high resolution and reduced training overhead.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"41 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":"122441230","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

Image Method Based 6G Channel Modeling for IIoT and Mobility Scenarios 基于图像方法的工业物联网和移动场景6G信道建模

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012888

Tianyi Liao, Tianyi Zhai, Haotian Zhang, Ruijia Li, Jialing Huang, Yuxiao Li, Yinghua Wang, Jie Huang, Chenghai Wang

{"title":"Image Method Based 6G Channel Modeling for IIoT and Mobility Scenarios","authors":"Tianyi Liao, Tianyi Zhai, Haotian Zhang, Ruijia Li, Jialing Huang, Yuxiao Li, Yinghua Wang, Jie Huang, Chenghai Wang","doi":"10.1109/VTC2022-Fall57202.2022.10012888","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012888","url":null,"abstract":"Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"46 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":"128069566","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

Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality 智慧城市的数字孪生:通过混合现实的案例研究和可视化

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012753

W. Piper, Hongjian Sun, Jing Jiang

{"title":"Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality","authors":"W. Piper, Hongjian Sun, Jing Jiang","doi":"10.1109/VTC2022-Fall57202.2022.10012753","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012753","url":null,"abstract":"Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city’s street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in real-time, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city’s traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean Root-Mean-Square Error and Mean Absolute Percentage Error values of 12.0% and 20.0% respectively.","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":"132560159","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

Towards Quantum Annealing for Multi-user NOMA-based Networks 基于多用户noma网络的量子退火研究

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012769

Eldar Gabdulsattarov, Khaled Maaiuf Rabie, Xingwang Li, G. Nauryzbayev

引用次数: 0

LSTM-based RIS Phase Shift Control for V2X Communication Systems 基于lstm的V2X通信系统RIS相移控制

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012864

Hyunsoo Kim, Y. Byun, B. Shim

引用次数: 0

Location-Dependent Task Bundling for Mobile Crowdsensing 基于位置的移动群体感知任务绑定

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013041

Yan Zhen, Yunfei Wang, Peng He, Yaping Cui, Ruyang Wang, Dapeng Wu

{"title":"Location-Dependent Task Bundling for Mobile Crowdsensing","authors":"Yan Zhen, Yunfei Wang, Peng He, Yaping Cui, Ruyang Wang, Dapeng Wu","doi":"10.1109/VTC2022-Fall57202.2022.10013041","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013041","url":null,"abstract":"The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"48 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":"134379502","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

Cooperative Positioning with the Aid of Reconfigurable Intelligent Surfaces and Zero Access Points 基于可重构智能曲面和零接入点的协同定位

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012935

Mustafa Ammous, S. Valaee

引用次数: 3

Fast Spectrum Sharing in Vehicular Networks: A Meta Reinforcement Learning Approach 车辆网络快速频谱共享:一种元强化学习方法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012705

Kai Huang, Zezhou Luo, Le Liang, Shi Jin

{"title":"Fast Spectrum Sharing in Vehicular Networks: A Meta Reinforcement Learning Approach","authors":"Kai Huang, Zezhou Luo, Le Liang, Shi Jin","doi":"10.1109/VTC2022-Fall57202.2022.10012705","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012705","url":null,"abstract":"In this paper, we investigate the resource allocation problem in a dynamic vehicular environment, where multiple vehicle-to-vehicle links attempt to reuse the spectrum of vehicle-to-infrastructure links. It is modeled as a deep reinforcement learning problem that is subject to proximal policy optimization. Training a well-performing policy usually requires a massive amount of interactions with the environment for a long time and thus is typically performed on a simulator. However, an agent well trained in a simulated environment may still fail when deployed in a live network, due to inevitable difference between the two environments, termed reality gap. We make preliminary efforts to address this issue by leveraging meta reinforcement learning that allows the learning agent to quickly adapt to a new environment with minimal interactions after being trained across a variety of similar tasks. We demonstrate that only a few episodes are required for the meta trained policy to adapt to a new environment and the proposed method is shown to achieve near-optimal performance and exhibit rapid convergence.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"65 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":"114694573","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

Energy-Efficient Symbiotic Radio Using Generalized Benders Decomposition 基于广义弯曲分解的节能共生无线电

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013073

Haoran Peng, Cheng-Yuan Ho, Yen-Ting Lin, Li-Chun Wang

{"title":"Energy-Efficient Symbiotic Radio Using Generalized Benders Decomposition","authors":"Haoran Peng, Cheng-Yuan Ho, Yen-Ting Lin, Li-Chun Wang","doi":"10.1109/VTC2022-Fall57202.2022.10013073","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013073","url":null,"abstract":"This paper investigates the symbiotic radio (SR) system supported by reconfigurable intelligent surfaces (RIS) to provide shared spectrum. SR Stakeholders share the same infrastructure and spectrum resources, but with different quality of service (QoS) requirements. The objective of this study is to develop a low complexity and global optimization algorithm to maximize the energy efficiency (EE) of the secondary receiver (SRx) and under a required signal-to-interference-plus-noise ratio (SINR) constraint for the primary receiver (PRx). Specifically, we formulate the joint optimization of phase shift, transmission power control, and reflection element scheduling of the RIS-assisted SR system as a nonconvex mixed-integer nonlinear program (MINLP) problem. Then, we relax the nonconvex MINLP problem into an equivalent convex MINLP problem. To this end, we propose an efficient and effective method based on the accelerated generalized Benders decomposition (GBD) algorithm to solve the global-optimal and fast convergence goals. Simulation results show that the proposed GBDbased approach efficiently improves the EE by 41.94% compared to the successive convex approximation (SCA).","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"49 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":"114471903","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

A Novel Malware Traffic Classification Method Based on Differentiable Architecture Search 一种基于可微架构搜索的恶意软件流量分类新方法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012863

Y. Shi, Xixi Zhang, Zhengran He, Jie Yang

{"title":"A Novel Malware Traffic Classification Method Based on Differentiable Architecture Search","authors":"Y. Shi, Xixi Zhang, Zhengran He, Jie Yang","doi":"10.1109/VTC2022-Fall57202.2022.10012863","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012863","url":null,"abstract":"The application of deep learning (DL) in the field of network intrusion detection (NID) has yielded remarkable results in recent years. As for malicious traffic classification tasks, numerous DL methods have proved robust and effective with self-designed model architecture. However, the design of model architecture requires substantial professional knowledge and effort of human experts. Neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal, which is a subdomain of automatic machine learning (AutoML). After that, Differentiable Architecture Search (DARTS) has been proposed by formulating architecture search in a differentiable manner, which greatly improves the search efficiency. In this paper, we introduce a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets. In addition, we compare the DARTS method with several common models, including convolutional neural network (CNN), full connect neural network (FC), support vector machine (SVM), and multi-layer Perception (MLP). Simulation results show that the proposed method can achieve the optimal classification accuracy at lower parameters without manual architecture engineering.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"58 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":"117253262","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