2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)最新文献_第9页

Image Generation from Scene Graph with Object Edges 从带有物体边缘的场景图生成图像

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012878

Chenxing Li, Yiping Duan, Qiyuan Du, Chengkang Pan, Guangyi Liu, Xiaoming Tao

{"title":"Image Generation from Scene Graph with Object Edges","authors":"Chenxing Li, Yiping Duan, Qiyuan Du, Chengkang Pan, Guangyi Liu, Xiaoming Tao","doi":"10.1109/VTC2022-Fall57202.2022.10012878","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012878","url":null,"abstract":"Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"11 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":"124637908","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

Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution 变形测试边缘实时人脸识别及入侵检测解决方案

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012836

Mourad Raif, El Mehdi Ouafiq, Abdessamad El Rharras, A. Chehri, Rachid Saadane

引用次数: 0

The Enhanced Sidelink Resource Reservation Mechanism of NR-V2X NR-V2X的增强型旁链路资源预留机制

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012897

Li Zhao, Jin-Ling Hu, Rui Zhao, Yan Shi

引用次数: 0

Age of Information Optimization in UAV-enabled Intelligent Transportation System via Deep Reinforcement Learning 基于深度强化学习的无人机智能交通系统信息优化时代

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012697

Xinmin Li, Jiahui Li, B. Yin, Jiaxin Yan, Yuan Fang

引用次数: 0

An Open-Source GNU Radio Framework for LoRa Physical Layer and Collision Resolution 用于LoRa物理层和冲突解决的开源GNU无线电框架

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013071

Weixuan Xiao, G. D. Sousa, N. Rachkidy, A. Guitton

引用次数: 0

A Novel Estimation Method of Radio Propagation Characteristics Based on Color Images 一种基于彩色图像的无线电传播特性估计方法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012968

Takahiro Tomie, Satoshi Suyama, K. Kitao, Mitsuki Nakamura

引用次数: 1

Joint Task Offloading and Resource Allocation in STAR-RIS assisted NOMA System STAR-RIS辅助NOMA系统中的联合任务卸载与资源分配

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013059

Liang Guo, Jie Jia, Jian Chen, An Du, Xingwei Wang

{"title":"Joint Task Offloading and Resource Allocation in STAR-RIS assisted NOMA System","authors":"Liang Guo, Jie Jia, Jian Chen, An Du, Xingwei Wang","doi":"10.1109/VTC2022-Fall57202.2022.10013059","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013059","url":null,"abstract":"In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"138 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":"126014970","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

Bilinear Approximate Message Passing Based Off-grid Channel Estimation for Multi-user Millimeter-Wave MIMO System 基于双线性近似消息传递的多用户毫米波MIMO离网信道估计

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012928

Yang Li, Shuyi Chen, W. Meng

引用次数: 0

Repetition-Based NOMA-HARQ with Adaptive Termination for URLLC URLLC中基于重复的自适应终止NOMA-HARQ

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013081

Go Takita, Takanori Hara, Y. Yuda, K. Higuchi

{"title":"Repetition-Based NOMA-HARQ with Adaptive Termination for URLLC","authors":"Go Takita, Takanori Hara, Y. Yuda, K. Higuchi","doi":"10.1109/VTC2022-Fall57202.2022.10013081","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013081","url":null,"abstract":"This paper proposes a repetition-based low-latency non-orthogonal multiple access (NOMA)-hybrid automatic repeat request (HARQ) method with adaptive termination for ultra-reliable low latency communications (URLLC). To reduce transmission delay, the proposed method retransmits packets in short transmission time intervals in advance and terminates packet retransmissions adaptively according to acknowledgement (ACK) feedback from the receiver. In addition, to reduce the throughput loss due to unnecessary retransmitted packets sent in the time until ACK feedback, retransmitted packets of the URLLC user are non-orthogonally multiplexed in the same channel with packets of other users based on NOMA. Two non-orthogonal multiplexing methods are compared: superposition coding (SPC) and joint modulation (JM). As for the receiver structure, a successive interference canceller (SIC) and complexity reduced maximum likelihood detection (R-ML) are investigated. We confirm that the proposed method using JM and R-ML provides the best improvement in the transmission delay time versus throughput trade-off based on computer simulations.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"99 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":"128771582","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

Physical-Layer-Security-based OFDM Transmission with Phase Error Insertion 基于物理层安全的相位错误插入OFDM传输

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012720

Ahmed Aladi, E. Alsusa

引用次数: 0