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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。毫米波 (mmWave) 是支持交通、娱乐、教育和远程医疗领域新应用的核心无线技术。最近，100 GHz 以上的廉价硬件的出现使得将 D 频段 (110-170 GHz) 毫米波网络推向大众的时机已经成熟。网络在优化微微蜂窝部署、移动链路的协调和适应以及网络成像前所未有的无中断融合方面带来了新的挑战。该研究项目解决了这些关键挑战，并提高了移动 D 频段网络的性能、可靠性和可用性。该项目将设计机器学习增强型可扩展 D 频段系统和网络，并将其集成到增强现实 (AR)、无人机送货和自动驾驶汽车等应用中，研究成果将影响更广泛的人群。通过以下方式：(1) 为服务不足的用户提供无处不在的高质量带宽；(2) 有效利用频谱，以更好地利用这一国家重要资源；(3) 通过启用多个关键应用来提高网络设备的利用率。拟议的研究将通过出版物、开源软件和数据集以及与行业合作伙伴的密切合作进行传播，并将通过设计新的本科生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融入教育。旨在使通过解决部署、链路自适应、协调和统一网络成像方面的基本挑战，D 频段毫米波网络和应用的实际采用具体而言，该项目通过彻底了解光学视觉和深度学习增强范例。 D 频段信道、构建测量驱动的经验和学习模型以及设计实用的实时系统将实现以下目标：(1) 优化部署和“假设”分析的框架。帮助优化 D 频段成本和效益的工具(2) 链路适应和协调协议可显着最小化延迟并最大程度地提高可扩展 D 频段网络的吞吐量和效率。 (3) 统一的网络成像协议可对吞吐量和延迟进行干扰。克服通道镜面反射的挑战，实现高分辨率 D 波段图像 该项目将在 D 波段测试台中设计、构建和实证验证所提出的系统，并且该测试台将扩展到一个增强知识的教育平台。无线和网络的该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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