Collaborative Research: SWIFT: SMALL: Learning-Efficient Spectrum Access for No-Sensing Devices in Shared Spectrum

该项目开发了一种新颖的基于在线学习的框架，用于分布式低成本设备，以高效和有效地访问共享频谱，而无需频谱感知。它特别关注无传感设备，这些设备没有强大的射频（RF）组件来实现宽带频谱传感，并解决分散环境下的跨技术频谱接入问题。该解决方案解决的一个相关应用是部署在未授权或轻度授权频谱上的物联网（IoT）设备的动态频谱访问，其中分布式物联网设备需要与其他活动系统共存。无感知频谱接入与共享框架有可能彻底改变现代和未来无线网络的运营和管理，极大地提高频谱利用效率，极大地缓解有限的无线电频谱不断增加的压力。该研究的跨学科性质自然会转化为由pi教授的通信、机器学习和网络领域的大量本科和研究生课程中的案例研究和项目。该项目旨在开发一套基于在线学习的频谱访问算法，用于无传感设备与其他有源系统共存。第一项研究的重点是通过引入最佳臂识别框架，提出元学习和良好的通道识别算法来提高学习效率。第二个重点是设计能够无缝集成混合自动重复请求（HARQ）的频谱接入机制。将设计新的算法来学习可能重传的最佳通道序列，并增强对捕获HARQ编码级行为的细粒度控制。研究的最后一条线索考虑了多用户多技术共存，并将开发基于隐式通信的分布式频谱接入算法。最后，将使用实验室测试平台和实际数据集对算法和频谱接入方案进行彻底验证。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。

This project develops a novel online learning based framework for distributed low-cost devices to efficiently and effectively access the shared spectrum without spectrum sensing. It specifically focuses on no-sensing devices that do not have the powerful radio-frequency (RF) components to enable wideband spectrum sensing, and addresses the cross-technology spectrum access problem in a decentralized setting. A pertinent application the proposed solution addresses is the dynamic spectrum access of Internet-of-Things (IoT) devices that are deployed in either unlicensed or lightly licensed spectrum, in which the distributed IoT devices need to coexist with other active systems. The no-sensing spectrum access and sharing framework has the potential to revolutionize the operation and management of modern and future wireless networks, considerably enhance the spectrum utilization efficiency, and dramatically alleviate the constantly increasing pressure on the limited radio spectrum. The cross disciplinary nature of the research would naturally translate into case studies and projects in a number of undergraduate and graduate level courses taught by the PIs in areas of communications, machine learning, and networking.This project aims to develop a suite of online learning based spectrum access algorithms for no-sensing devices to coexist with other active systems. The first study focuses on improving the learning efficiency by introducing the best arm identification framework and proposing meta-learning and good channel identification algorithms. The second thrust is devoted to designing spectrum access mechanisms that can seamlessly integrate hybrid automatic repeat request (HARQ). Novel algorithms will be designed to learn the optimal sequence of channels for possible retransmissions, and enhanced for fine-grained control that captures the coding level behavior of HARQ. The last thread of investigation considers multi-user multi-technology coexistence and will develop implicit-communication based distributed spectrum access algorithms. Finally, a thorough validation of the algorithms and spectrum access schemes will be performed using a lab testbed and real-world datasets.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.