2019 IEEE International Conference on Communications Workshops (ICC Workshops)最新文献_第5页

The Application of Multi-Agent Reinforcement Learning in UAV Networks 多智能体强化学习在无人机网络中的应用

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756984

Jingjing Cui, Yuanwei Liu, A. Nallanathan

引用次数: 20

Low Resolution Phase Shifters Suffice for Full-Duplex mmWave Communications 低分辨率移相器足以满足全双工毫米波通信

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756743

J. M. B. D. Silva, A. Sabharwal, Gábor Fodor, C. Fischione

引用次数: 3

Blockchain-Driven Contents Sharing Strategy for Wireless Cache-Enabled D2D Networks 区块链驱动的无线缓存D2D网络内容共享策略

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8757177

Huan Cui, Zhiyong Chen, Ning Liu, Bin Xia

引用次数: 16

Achievable Rate Bounds on Poisson Channel with a Sample-Based Practical Photon-Counting Receiver 基于样本的实用光子计数接收机在泊松信道上可实现的速率边界

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8757168

Zhimeng Jiang, Chen Gong, Guanchu Wang, Zhengyuan Xu

引用次数: 2

Impact of Analog Precoding Types on the Spectral Efficiency of Millimeter Wave Hybrid Beamforming Systems 模拟预编码类型对毫米波混合波束形成系统频谱效率的影响

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8757086

Pan Tang, Jian-hua Zhang, Lei Tian

引用次数: 0

Dynamic Hand Gesture Detection and Recognition with WiFi Signal Based on 1D-CNN 基于1D-CNN的WiFi信号动态手势检测与识别

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756690

Xuan-qi Pan, Ting Jiang, Xudong Li, Xue Ding, Yangyang Wang, Yanan Li

{"title":"Dynamic Hand Gesture Detection and Recognition with WiFi Signal Based on 1D-CNN","authors":"Xuan-qi Pan, Ting Jiang, Xudong Li, Xue Ding, Yangyang Wang, Yanan Li","doi":"10.1109/ICCW.2019.8756690","DOIUrl":"https://doi.org/10.1109/ICCW.2019.8756690","url":null,"abstract":"Due to the rapid development of Internet of Things (IoT) technology and artificial intelligence, there is an urgent need for human-computer interaction (HCI) applications. The dynamic hand gesture recognition technology based on WiFi signal plays an important role. However, although the gesture recognition system using Channel State Information (CSI) has made great progress, we have observed that in the current research, most commercial network cards can not directly extract such signals, and the easily acquired received signal strength (RSS) can only recognize simple gestures. Therefore, in this paper, we present a universal framework to achieve dynamic hand gesture detection and recognition with RSS. We use RSS from multiple independent WiFi nodes to increase the upper limit of recognize capability, enabling the system to recognize seven complex dynamic hand gestures. The false trigger detection algorithm effectively eliminates the false triggers, and the detection accuracy is close to 91.38%. The system uses a state machine and a linear scale algorithm to accommodate different hand gesture speed with durations ranging from 0.9s to 5.4s. Furthermore, we analyze the errors of the detection algorithm and propose a recognition architecture based on One Dimension Convolutional Neural Network (1D-CNN) and two data collection strategies: gesture extending and gesture shifting. The proposed 1D-CNN effectively overcomes the error caused by hand gesture detection algorithm and the recognition accuracy reaches 86.91%. Combined with the gesture shifting strategy, the recognition accuracy is further improved to 93.03%.","PeriodicalId":426086,"journal":{"name":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125009076","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

Polarimetric Characterization of Sub-6-GHz Self-Interference Indoor Radio Channels sub - 6ghz自干扰室内无线电信道的极化特性研究

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756646

Ramez Askar, Fabian Undi, Rodrigo Silva Rezende, M. Peter, W. Keusgen

{"title":"Polarimetric Characterization of Sub-6-GHz Self-Interference Indoor Radio Channels","authors":"Ramez Askar, Fabian Undi, Rodrigo Silva Rezende, M. Peter, W. Keusgen","doi":"10.1109/ICCW.2019.8756646","DOIUrl":"https://doi.org/10.1109/ICCW.2019.8756646","url":null,"abstract":"To obtain a better understanding of full-duplex wireless systems, this paper studies the behavior of the self-interference radio channels. It presents a physical model of the self-interference radio channel that splits the channel into two segments — a quasi-static direct-coupling-based and a dynamic backscatter-caused. Self-interference channel measurements were performed in a modern-office indoor environment. Additionally, the antenna direct-coupling component is measured in a semi-anechoic chamber. The captured measurement data are used to characterize three orthogonally-coexisting polarimetric self-interference channel components — two co-polarized and one cross-polarized. One gigahertz of sounding bandwidth, which occupies 2-to-3-GHz frequency band, is used to measure the self-interference channels. A dually-polarized-magnetoelectric-dipole antenna is utilized to observe the self-interference backscatter channels; the antenna possesses a hemispherical radiation pattern and an excellent cross-polarization discrimination property. Both incident and reflected RF wave quantities — captured by means of a vector network analyzer on the two ports of the antenna — have been used to acquire a channel knowledge of all three polarimetric components. Instantaneous and averaged power-delay profiles of the measured polarimetric self-interference channels are presented. Moreover, statistical parameters of indoor de-embedded backscatter channels are extracted.","PeriodicalId":426086,"journal":{"name":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128929259","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: 9

The Role of Data Repositories in Named Data Networking 数据存储库在命名数据网络中的作用

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756944

Lixia Zhang

引用次数: 2

Error Performance of Orthogonal Time Frequency Space (OTFS) Modulation 正交时频空间(OTFS)调制的误差性能

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756831

E. Biglieri, P. Raviteja, Y. Hong

引用次数: 22

A Synthetic User Behavior Dataset Design for Data-Driven AI-Based Personalized Wireless Networks 基于数据驱动人工智能的个性化无线网络用户行为综合数据集设计

2019 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2019-05-20 DOI: 10.1109/ICCW.2019.8756804

R. Alkurd, I. Abualhaol, H. Yanikomeroglu

引用次数: 6