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

通过车辆之间以及与交通基础设施之间的智能连接，未来的交通将更安全、更高效。同时，旅行者也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。所有这一切的关键是一个可靠和弹性的车联网（IoV）。由于独特的交通环境，一个令人满意的车联网面临着许多挑战。例如，运输安全/效率和/或旅行者便利/舒适的数据服务往往对延迟非常敏感，需要大带宽。车辆环境也充满了各种通信服务以及主动传感设备，这些设备可能会相互造成干扰。此外，交通固有的高流动性和交通信息交换随特定交通场景的波动，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，该项目组织了合作努力，以提高车联网中的频谱利用、共享和管理。该项目将促进多学科专家之间的互动，如电磁波、电子学、信号处理和无线通信，以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接受。该项目还有一个综合教育计划，旨在帮助劳动力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领域的多样性。该项目旨在通过一种新型动态射频前端开发具有同步传感和通信功能的认知车联网框架。针对上述挑战，提出的车联网研究有三个显著特点。首先，提出的研究以同步通信和传感为中心。基于创新设计的动态RF前端，以促进全双工模式，通信和同时监控具有可调粒度的多个频段成为可能。其次，由此产生的车联网框架在两个方面是认知的：i)频谱环境中的认知-来自配备动态射频前端的设备的频谱感知信息用于开发算法，以学习和跟踪具有可量化不确定性的时空无线电断层扫描；ii)物理环境中的认知-通过明智地设计波形，使同时通信和主动物理环境感知成为可能，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，提出的认知车联网框架是动态准备通过硬件，架构和算法设计：动态射频前端具有实时调谐和控制能力，网络架构集成了无人机（UAV），以调动对交通/旅行者数据服务热点的按需支持，而用于实现频谱资源闭环控制和管理的强化学习算法在动态未知或不可预测时仍将保持鲁棒性。该奖项反映了美国国家科学基金会的法定使命，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.