Collaborative Research: SWIFT: Cognitive-IoV with Simultaneous Sensing and Communications via Dynamic RF Front End

通过车辆之间的互联智能以及交通基础设施，未来的交通有望实现更安全、更高效的出行。同时，出行者还可以从繁琐的驾驶中解脱出来，在移动中将车辆用作办公室或娱乐室。对所有这些都至关重要的是可靠且有弹性的车联网(IoV)。鉴于独特的交通环境，令人满意的IoV运行面临许多挑战。例如，用于交通安全/效率和/或旅客便利/舒适的数据服务通常对延迟非常敏感，并且需要很大的带宽。车辆环境中还充斥着各种通信服务以及有源传感设备，这可能会导致相互干扰。此外，交通运输固有的高机动性和交通信息交换的波动取决于特定的交通情景，这两者都可能导致快速变化的动态，甚至可能是不可预测的动态。为了应对这些挑战，该项目组织了合作努力，以增强IoV中的频谱利用、共享和管理。该项目将促进电磁波、电子学、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。所开发的技术将为基础科学和工程研究提供宝贵的工具，并促进社会对新兴认知物联网技术的接受。该项目还有一个综合教育计划，旨在为劳动力做好准备，以应对未来的频谱利用和无线通信挑战，同时促进和拥抱科学和工程方面的多样性。该项目旨在开发一个认知IoV框架，通过一个新颖的动态射频前端同时进行感知和通信。针对上述挑战，拟议的IoV研究具有三个鲜明的特点。首先，拟议的研究集中在同时通信和感知上。基于创新地设计以促进全双工模式的动态射频前端，通信和同时监控具有可调粒度的多个频段成为可能。其次，由此产生的IoV框架在两个方面是认知的：i)频谱环境中的认知-使用来自配备动态射频前端的设备的频谱感知信息来开发算法，以学习和跟踪具有可量化不确定性的时空无线电断层扫描；以及ii)物理环境中的认知-通过精心设计的波形实现同时通信和主动物理环境感知，所获取的信息将被利用来增强通信。最后但并非最不重要的是，拟议的认知物联网框架通过硬件、架构和算法设计为动态做好了准备：动态射频前端拥有实时调谐和控制能力，网络架构结合了无人机(UAV)以动员对交通/旅行者数据服务热点的按需支持，并且为实现频谱资源的闭环控制和管理而开发的强化学习算法将在动态未知或不可预测的情况下保持稳健。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.