CAREER: Vision and Learning Augmented D-Band Networking and Imaging

职业：视觉和学习增强 D 波段网络和成像

• 批准号: 2144505
• 负责人: Sanjib Sur
• 金额: $ 56万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144505&HistoricalAwards=false
• 关键词: CAREER; Vision; Learning; Augmented; Band

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Millimeter-wave (mmWave) is the core wireless technology to enable new applications in transportation, entertainment, education, and telemedicine. Specifically, the recent availability of inexpensive hardware above 100 GHz makes the time ripe for bringing D-band (110-170 GHz) mmWave networks to the masses. However, D-band mmWave networks bring new challenges in optimizing the deployment of picocells, coordination and adaptation of mobile links with unprecedentedly wide frequency options, and a disruption-free confluence of networking-imaging. This research project addresses these key challenges and improves the performance, reliability, and usability of mobile D-band networks. The project will design machine learning augmented scalable D-band systems and networks, and integrate them into applications, such as Augmented Reality (AR), drone delivery, and autonomous cars. The research outcomes will impact the broader population by: (1) bringing ubiquitous and high-quality bandwidth to underserved users; (2) enabling efficient use of spectrum to better utilize this nationally important resource; and (3) elevating the utility of networking devices by enabling several critical applications on them. The proposed research will be disseminated through publications, open-source software and datasets, and close collaboration with industry partners. It will be integrated into education by designing new undergraduate and graduate cross-disciplinary wireless curricula and involvement in broader community outreach activities.This project aims to enable the practical adoption of D-band mmWave networks and applications by solving the fundamental challenges in deployment, link adaptation, coordination, and unified networking-imaging. Specifically, the project explores an optical vision and deep learning augmented paradigm by thoroughly understanding the physical properties of the D-band channel, building measurement-driven empirical and learning models, and designing practical, real-time systems. Successful execution of this project would enable the following. (1) A framework for optimal deployment and a “what-if” analysis tool to help optimize the cost and benefits of D-band deployment in both indoor and outdoor environments. (2) Link adaptation and coordination protocols that significantly minimize latency and maximize throughput and efficiency for scalable D-band networking. (3) A unified networking-imaging protocol that reduces disruptions to the throughput and latency and overcomes challenges with the channel specularity to enable high-resolution D-band images. The project will design, build, and empirically validate the proposed systems in a D-band testbed, and the testbed will be extended into an educational platform that enhances the knowledge of wireless networking and sensing for students at different levels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》（公共法第117-2）的全部或部分资助的。毫米波（MMWave）是核心无线技术，旨在为运输，娱乐，教育和远程医疗提供新的应用。具体而言，最近廉价的硬件在100 GHz以上的可用性使得将D波段（110-170 GHz）MMWAVE网络带到大众的时间已经成熟。但是，D波段MMWave网络带来了新的挑战，可以优化Picocells的部署，具有前所未有的频率选项的移动链接的协调和适应，以及网络成像的无干扰汇合。该研究项目应对这些关键挑战，并提高移动D波段网络的性能，可靠性和可用性。该项目将设计机器学习增强可扩展的D波段系统和网络，并将它们集成到应用程序中，例如增强现实（AR），无人机交付和自动驾驶汽车。研究结果将通过以下方式影响更广泛的人群：（1）将无处不在和高质量的带宽带给服务不足的用户； （2）有效利用频谱来更好地利用这一国家重要的资源； （3）通过对它们启用几个关键应用程序来提高网络设备的实用性。拟议的研究将通过出版物，开源软件和数据集进行传播，并与行业合作伙伴进行密切合作。它将通过设计新的本科和研究生跨学科无线课程以及参与更广泛的社区外展活动来整合到教育中。​​该项目旨在通过解决D波段MMWave网络和应用程序实践，通过解决部署，协调，统一的网络和统一的挑战，并探索融合的挑战，并探索统一的挑战。范式通过基本了解D波段通道的物理特性，建立测量驱动的经验和学习模型以及设计实用的实时系统。该项目的成功执行将实现以下内容。 （1）一个最佳部署的框架和一个“何时”分析工具，可帮助优化室内和室外环境中D波段部署的成本和收益。 （2）链接适应和协调协议，可显着最大程度地减少潜伏期，并最大程度地提高可扩展的D波段网络的吞吐量和效率。 （3）一种统一的网络成像协议，可减少对吞吐量和延迟的破坏，并通过通道镜面克服挑战，以启用高分辨率D波段图像。该项目将在经过测试的D波段中设计，构建和经验验证拟议的系统，并且该测试将扩展到一个教育平台，从而增强了对不同级别的学生对无线网络和敏感性的了解。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务，并通过评估诚实地进行了评估。

项目成果

Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment

迈向深度学习增强稳健 D 频段毫米波微微蜂窝部署

• DOI: 10.1145/3595244.3595266
• 发表时间: 2023
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Regmi, Hem;Sur, Sanjib
• 通讯作者: Sur, Sanjib

D3PicoNet: Deep Learning Networks for Robust Deployment of D-Band Millimeter-Wave Picocells

D3PicoNet：用于 D 频段毫米波微微蜂窝鲁棒部署的深度学习网络

• 发表时间: 2023
• 期刊: IEEE WoWMoM
• 作者: Regmi, H.;Sur, S.
• 通讯作者: Sur, S.

Exploring the Potential of Residual Networks for Efficient Sub-Nyquist Spectrum Sensing

探索残差网络实现高效亚奈奎斯特频谱传感的潜力

• DOI: 10.1109/wimob58348.2023.10187871
• 发表时间: 2023
• 期刊: IEEE
• 作者: Regmi, Hem;Sur, Sanjib
• 通讯作者: Sur, Sanjib

SugarWave: A Non-destructive Estimation of Fruit Sugar Content Using Millimeter-Wave Sensing

SugarWave：利用毫米波传感无损估算水果糖含量

• DOI: 10.1109/mass58611.2023.00079
• 发表时间: 2023
• 期刊: IEEE
• 作者: Tavasoli, Reza;Sur, Sanjib;Nelakuditi, Srihari
• 通讯作者: Nelakuditi, Srihari

FlexVAA: A Flexible, Passive van Atta Retroreflector for Roadside Infrastructure Tagging and Identification

FlexVAA：用于路边基础设施标记和识别的灵活、无源 van Atta 后向反射器

• DOI: 10.1145/3560905.3568089
• 发表时间: 2022
• 期刊: ACM
• 作者: Junker, Nicholas;Ge, Jinqun;Wang, Guoan;Sur, Sanjib
• 通讯作者: Sur, Sanjib