Collaborative Research: SWIFT: Cognitive-IoV with Simultaneous Sensing and Communications via Dynamic RF Front End
合作研究:SWIFT:通过动态射频前端实现同步传感和通信的认知车联网
基本信息
- 批准号:2128593
- 负责人:
- 金额:$ 42万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-15 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Future transportation is promising safer and more efficient traveling via connected intelligence among vehicles as well as with the transportation infrastructure. At the same time, travelers can also be relieved from tedious driving and use vehicles as offices or entertainment rooms on the move. Essential to all these is a reliable and resilient Internet of Vehicles (IoV). Given the unique transportation environment, a satisfactorily functioning IoV is confronted with many challenges. For example, data services for transportation safety/efficiency and/or traveler convenience/comfort are often very sensitive to delays and require large bandwidth. The vehicular environments are also filled with various communication services as well as active sensing devices, which can potentially cause interferences to each other. In addition, the high mobility inherent to transportation and fluctuation of the transportation information exchange depending on the specific traffic scenario can both lead to fast-changing and possibly unpredictable dynamics. To address these challenges, this project organizes collaborative efforts to enhance spectrum utilization, sharing and management in IoV. The project will promote the interactions among multi-disciplinary experts such as electromagnetic waves, electronics, signal processing and wireless communications to create wireless innovations at different network layers. The developed technologies will provide valuable tools for foundational science and engineering research and promote societal embracing of the emergent cognitive IoV technologies. The project also has an integrated education plan that aims to prepare the workforce to address future challenges of spectrum utilization and wireless communications, while promoting and embracing diversity in science and engineering.This project aims to develop a cognitive IoV framework with simultaneous sensing and communications via a novel dynamic RF front end. Targeting the aforementioned challenges, the proposed IoV research has three distinctive features. First, the proposed research is centered at simultaneous communications and sensing. Based on a dynamic RF front end that is innovatively designed to facilitate full duplex modes, communications and simultaneous monitoring of multiple spectrum bands with tunable granularity become possible. Secondly, the resultant IoV framework is cognitive in two counts: i) cognitive in the spectrum environment - the spectrum sensing information from devices equipped with the dynamic RF front end is used to develop algorithms to learn and track the spatiotemporal radio tomography with quantifiable uncertainty; and ii) cognitive in the physical environment - with judiciously designed waveforms that enable simultaneous communications and active physical environment sensing, the acquired information will be leveraged to enhance communications. Last but not least, the proposed cognitive IoV framework is dynamics-ready via hardware, architecture, and algorithm design: the dynamic RF front end boasts real-time tuning and control capability, the network architecture incorporates unmanned aerial vehicles (UAV) to mobilize on-demand support for transportation/traveler data service hotspots, and reinforcement learning algorithms developed to achieve closed-loop control and management of spectrum resources will remain robust when the dynamics are unknown or unpredictable.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.
未来的交通运输有望通过车辆之间以及交通基础设施之间的连接智能实现更安全,更高效的旅行。 同时,旅行者也可以从繁琐的驾驶中解脱出来,在旅途中将车辆用作办公室或娱乐室。所有这些都离不开可靠且有弹性的车联网(IoV)。鉴于独特的交通环境,一个令人满意的车联网运行面临着许多挑战。例如,用于运输安全/效率和/或旅行者便利/舒适的数据服务通常对延迟非常敏感并且需要大带宽。车辆环境中还充满了各种通信服务以及有源传感设备,这些设备可能会相互干扰。此外,运输固有的高流动性和取决于特定交通情况的运输信息交换的波动都可能导致快速变化和可能不可预测的动态。为了应对这些挑战,该项目组织合作努力,以提高车联网中的频谱利用率,共享和管理。该项目将促进电磁波、电子、信号处理和无线通信等多学科专家之间的互动,在不同的网络层创造无线创新。开发的技术将为基础科学和工程研究提供有价值的工具,并促进社会对新兴认知车联网技术的拥抱。该项目还制定了一项综合教育计划,旨在培养员工应对频谱利用和无线通信的未来挑战,同时促进和拥抱科学和工程的多样性。该项目旨在开发一个认知车联网框架,通过一个新颖的动态RF前端同时进行感知和通信。针对上述挑战,拟议的车联网研究有三个鲜明的特点。首先,拟议的研究集中在同时通信和传感。基于创新设计的动态RF前端,可实现全双工模式,通信和同时监控多个频谱带,粒度可调。其次,所得到的IoV框架在两个方面是认知的:i)频谱环境中的认知-来自配备有动态RF前端的设备的频谱感测信息用于开发算法以学习和跟踪具有可量化的不确定性的时空无线电断层扫描;和ii)在物理环境中的认知-通过明智地设计能够同时进行通信和主动物理环境感测的波形,将利用所获取的信息来增强通信。最后但并非最不重要的是,所提出的认知车联网框架通过硬件、架构和算法设计是动态就绪的:动态射频前端具有实时调谐和控制能力,网络架构采用无人机(UAV),以调动对交通/旅行者数据服务热点的按需支持,和强化学习算法,以实现封闭的,当动态未知或不可预测时,频谱资源的环路控制和管理将保持稳健。基金会的使命是履行其使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评价,被认为值得支持。
项目成果
期刊论文数量(16)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Incremental Ensemble Gaussian Processes
- DOI:10.1109/tpami.2022.3157197
- 发表时间:2021-10
- 期刊:
- 影响因子:23.6
- 作者:Qin Lu;G. V. Karanikolas;G. Giannakis
- 通讯作者:Qin Lu;G. V. Karanikolas;G. Giannakis
Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process
- DOI:10.1109/tpami.2023.3264741
- 发表时间:2022-05
- 期刊:
- 影响因子:23.6
- 作者:Qin Lu;Konstantinos D. Polyzos;Bingcong Li;G. Giannakis
- 通讯作者:Qin Lu;Konstantinos D. Polyzos;Bingcong Li;G. Giannakis
Bayesian Optimization with Ensemble Learning Models and Adaptive Expected Improvement
- DOI:10.1109/icassp49357.2023.10095008
- 发表时间:2023-06
- 期刊:
- 影响因子:0
- 作者:Konstantinos D. Polyzos;Qin Lu;G. Giannakis
- 通讯作者:Konstantinos D. Polyzos;Qin Lu;G. Giannakis
Ensemble Gaussian Processes for Online Learning Over Graphs With Adaptivity and Scalability
- DOI:10.1109/tsp.2021.3122095
- 发表时间:2022
- 期刊:
- 影响因子:5.4
- 作者:Konstantinos D. Polyzos;Qin Lu;G. Giannakis
- 通讯作者:Konstantinos D. Polyzos;Qin Lu;G. Giannakis
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
- DOI:10.1609/aaai.v37i9.26337
- 发表时间:2023-03
- 期刊:
- 影响因子:0
- 作者:Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis
- 通讯作者:Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis
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Georgios Giannakis其他文献
Georgios Giannakis的其他文献
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{{ truncateString('Georgios Giannakis', 18)}}的其他基金
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IMR: MM-1C: Learning-driven Models for 5G Internet Measurements
IMR:MM-1C:5G 互联网测量的学习驱动模型
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CCSS: Online Learning for IoT Monitoring and Management
CCSS:物联网监控和管理在线学习
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$ 42万 - 项目类别:
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Hybrid mmWave mMIMO Transceiver Design for Doubly-Selective Channels
适用于双选通道的混合毫米波 mMIMO 收发器设计
- 批准号:
2102312 - 财政年份:2020
- 资助金额:
$ 42万 - 项目类别:
Standard Grant
CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
CPS:中:协作研究:主动自动驾驶集体智慧 (CI-PAD)
- 批准号:
2103256 - 财政年份:2020
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Standard Grant
CIF: Medium: Adaptive Diffusions for Scalable and Robust Learning over Graphs
CIF:中:用于图上可扩展和鲁棒学习的自适应扩散
- 批准号:
1901134 - 财政年份:2019
- 资助金额:
$ 42万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Learn-and-Adapt to Manage Dynamic Cyber-Physical Networks
CCSS:协作研究:学习和适应管理动态信息物理网络
- 批准号:
1711471 - 财政年份:2017
- 资助金额:
$ 42万 - 项目类别:
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CCSS: Collaborative Research: Smart-Grid Powered Green Communications in Heterogeneous Networks
CCSS:协作研究:异构网络中智能电网驱动的绿色通信
- 批准号:
1508993 - 财政年份:2015
- 资助金额:
$ 42万 - 项目类别:
Standard Grant
EAGER-DynamicData: Judicious Censoring, Random Sketching, and Efficient Validate for Learning Patterns from Dynamically-Changing and Large-Scale Data Sets
EAGER-DynamicData:明智的审查、随机草图和高效验证,用于从动态变化的大规模数据集中学习模式
- 批准号:
1500713 - 财政年份:2015
- 资助金额:
$ 42万 - 项目类别:
Standard Grant
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