Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems最新文献

mmBAC: Location-aided mmWave Backhaul Management for UAV-based Aerial Cells mmBAC:定位辅助毫米波回程管理，用于基于无人机的空中蜂窝

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356763

L. Bertizzolo, Michele Polese, Leonardo Bonati, A. Gosain, M. Zorzi, T. Melodia

引用次数: 23

Learning Congestion State For mmWave Channels 学习毫米波信道拥塞状态

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356769

Talal Ahmad, Shiva R. Iyer, L. Díez, Y. Zaki, Ramón Agüero, L. Subramanian

引用次数: 3

Real-time Multi-Gigahertz Sub-Nyquist Spectrum Sensing System for mmWave 毫米波实时多千兆赫次奈奎斯特频谱传感系统

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356767

Zihang Song, Haoran Qi, Yue Gao

引用次数: 12

28 GHz Channel Measurements in the COSMOS Testbed Deployment Area COSMOS试验台部署区域的28ghz信道测量

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356770

Tingjun Chen, Manav Kohli, Tianyi Dai, A. Estigarribia, D. Chizhik, Jinfeng Du, R. Feick, R. Valenzuela, G. Zussman

{"title":"28 GHz Channel Measurements in the COSMOS Testbed Deployment Area","authors":"Tingjun Chen, Manav Kohli, Tianyi Dai, A. Estigarribia, D. Chizhik, Jinfeng Du, R. Feick, R. Valenzuela, G. Zussman","doi":"10.1145/3349624.3356770","DOIUrl":"https://doi.org/10.1145/3349624.3356770","url":null,"abstract":"Next generation wireless and mobile networks will utilize millimeter-wave (mmWave) communication to achieve significantly increased data rates. However, since mmWave radio signals experience high path loss, the operation of mmWave networks will require accurate channel models designed for specific deployment sites. In this paper, we focus on the deployment area of the PAWR COSMOS testbed [1, 2] in New York City and report extensive 28 GHz channel measurements. These include over 24 million power measurements collected from over 1,500 links on 13 sidewalks in 3 different sites and in different settings during March-June, 2019. Using these measurements, we study the effects of the setup and environments (e.g., transmitter height and seasonal effects). We then discuss the obtained path gain values and their fitted lines, and the resulting effective azimuth beamforming gain. Based on these results, we also study the link SNR values that can be supported on individual sidewalks and the corresponding theoretically achievable data rates. We believe that the results can inform the COSMOS testbed deployment process and provide a benchmark for other deployment efforts in dense urban areas.","PeriodicalId":330512,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127283168","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

On the Outage Probability of Millimeter Wave Links with Quasi-deterministic Propagation 准确定性传播毫米波链路的中断概率

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356762

Amr A. Abdelnabi, V. Mancuso, M. Marsan

引用次数: 2

mmSense: Multi-Person Detection and Identification via mmWave Sensing mmSense:通过毫米波传感进行多人检测和识别

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356765

Tianbo Gu, Zheng Fang, Zhicheng Yang, Pengfei Hu, P. Mohapatra

{"title":"mmSense: Multi-Person Detection and Identification via mmWave Sensing","authors":"Tianbo Gu, Zheng Fang, Zhicheng Yang, Pengfei Hu, P. Mohapatra","doi":"10.1145/3349624.3356765","DOIUrl":"https://doi.org/10.1145/3349624.3356765","url":null,"abstract":"In recent years, millimeter-wave (mmWave) is becoming a significant component of the next-generation wireless communication due to its up to 7 Gbps transmission rate. In addition to the communication benefits, the unique sensing feature of mmWave attracts more attention. Nowadays, the services of human detection and identification are needed in numerous application scenarios, such as smart home and smart industry. The RF-based sensing techniques, especially WiFi-based, are widely utilized in human detection and identification. However, these work either require humans to carry devices or cannot detect and identify multiple people simultaneously. In this paper, we propose mmSense, a device-free multi-person detection and identification framework, which exploits the unique mmWave sensing features. First, we utilize the properties of directionality, impenetrability, and reflection of 60 GHz signal for objects to fingerprint the environments. Based on the generated environment fingerprints with and without human presence, mmSense can detect and localize the presence of multiple people simultaneously via the LSTM-based classification model. Moreover, we propose a novel approach to use humans' outline profile and vital signs to identify multiple people by using 60 GHz reflected signals of the human body. We conduct extensive experiments to demonstrate the low-cost and effectiveness of our approach.","PeriodicalId":330512,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148573","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: 30

RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar RadHAR:通过毫米波雷达生成的点云来识别人类活动

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356768

Akash Deep Singh, S. Sandha, Luis Garcia, M. Srivastava

{"title":"RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar","authors":"Akash Deep Singh, S. Sandha, Luis Garcia, M. Srivastava","doi":"10.1145/3349624.3356768","DOIUrl":"https://doi.org/10.1145/3349624.3356768","url":null,"abstract":"Accurate human activity recognition (HAR) is the key to enable emerging context-aware applications that require an understanding and identification of human behavior, e.g., monitoring disabled or elderly people who live alone. Traditionally, HAR has been implemented either through ambient sensors, e.g., cameras, or through wearable devices, e.g., a smartwatch, with an inertial measurement unit (IMU). The ambient sensing approach is typically more generalizable for different environments as this does not require every user to have a wearable device. However, utilizing a camera in privacy-sensitive areas such as a home may capture superfluous ambient information that a user may not feel comfortable sharing. Radars have been proposed as an alternative modality for coarse-grained activity recognition that captures a minimal subset of the ambient information using micro-Doppler spectrograms. However, training fine-grained, accurate activity classifiers is a challenge as low-cost millimeter-wave (mmWave) radar systems produce sparse and non-uniform point clouds. In this paper, we propose RadHAR, a framework that performs accurate HAR using sparse and non-uniform point clouds. RadHAR utilizes a sliding time window to accumulate point clouds from a mmWave radar and generate a voxelized representation that acts as input to our classifiers. We evaluate RadHAR using a low-cost, commercial, off-the-shelf radar to get sparse point clouds which are less visually compromising. We evaluate and demonstrate our system on a collected human activity dataset with 5 different activities. We compare the accuracy of various classifiers on the dataset and find that the best performing deep learning classifier achieves an accuracy of 90.47%. Our evaluation shows the efficacy of using mmWave radar for accurate HAR detection and we enumerate future research directions in this space.","PeriodicalId":330512,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809236","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: 111

A First Look at 802.11ad Performance on a Smartphone 首先看看智能手机上的802.11ad性能

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 2019-10-07 DOI: 10.1145/3349624.3356766

Shivang Aggarwal, Arvind Thirumurugan, Dimitrios Koutsonikolas

引用次数: 10

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems 第三届美国计算机学会毫米波网络与传感系统研讨会论文集

Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems Pub Date : 1900-01-01 DOI: 10.1145/3349624

引用次数: 0