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：
Georgios Giannakis
金额：
$ 42万
依托单位：
University of Minnesota-Twin Cities
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：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128593&HistoricalAwards=false
关键词：
Collaborative Research SWIFT Cognitive IoV

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.

未来的运输是通过车辆之间的连接情报以及运输基础设施的连接智能，更安全，更有效的旅行。同时，旅行者也可以摆脱繁琐的驾驶，并将车辆用作办公室或娱乐室。所有这些都是可靠且具有弹性的车辆互联网（IOV）。鉴于独特的运输环境，令人满意的功能性IOV面临许多挑战。例如，用于运输安全/效率和/或旅行者便利/舒适性的数据服务通常非常敏感，并且需要大带宽。车辆环境还充满了各种通信服务以及主动的传感设备，这可能会彼此引起干扰。此外，根据特定的交通情况，运输和运输信息交换的固有的高移动性既可能导致快速变化，也可能导致无法预测的动态。为了应对这些挑战，该项目组织了协作努力，以增强IOV的频谱利用，共享和管理。该项目将促进多学科专家之间的互动，例如电磁波，电子，信号处理和无线通信，以在不同的网络层上创建无线创新。开发的技术将为基础科学和工程研究提供宝贵的工具，并促进对新兴认知IOV技术的社会拥抱。该项目还制定了一项综合教育计划，旨在准备劳动力，以应对光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领域的多样性。该项目旨在通过新颖的动态RF前端开发同时感应和通信的认知IOV框架。针对上述挑战，提出的IOV研究具有三个独特的特征。首先，拟议的研究集中在同时通信和传感上。基于动态的RF前端，其创新设计可促进完整的双链模式，可以使用可调粒度的多个光谱频段进行通信和同时监视。其次，最终的IOV框架在两个计数中具有认知：i）在频谱环境中的认知 - 配备有动态RF前端设备的频谱传感信息用于开发算法，以学习和跟踪具有可量化不确定性的时空公开摄影术； ii）在物理环境中的认知 - 具有明智设计的波形，可以同时进行通信和主动的物理环境感知，将利用获得的信息来增强通信。 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当动态是未知或不可预测的时，请保持健壮。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

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）

Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process

DOI：
10.1109/tpami.2023.3264741
发表时间：
2022-05
期刊：
IEEE Transactions on Pattern Analysis and Machine Intelligence
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

Incremental Ensemble Gaussian Processes

DOI：
10.1109/tpami.2022.3157197
发表时间：
2021-10
期刊：
IEEE Transactions on Pattern Analysis and Machine Intelligence
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

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：
IEEE Transactions on Signal Processing
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

Bayesian Optimization with Ensemble Learning Models and Adaptive Expected Improvement

DOI：
10.1109/icassp49357.2023.10095008
发表时间：
2023-06
期刊：
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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

Identifying Dependent Annotators in Crowdsourcing

识别众包中的依赖注释器