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

合作研究：SWIFT：SMALL：共享频谱中无感知设备的学习高效频谱访问

基本信息

批准号：
2029978
负责人：
Cong Shen
金额：
$ 21.96万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029978&HistoricalAwards=false
关键词：
Collaborative Research SWIFT SMALL Learning

项目摘要

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.

该项目开发了一种新的基于在线学习的框架，用于分布式低成本设备高效地访问共享频谱，而无需频谱感知。它特别关注不具有强大射频（RF）组件的无传感设备，以实现宽带频谱感知，并解决分散设置中的跨技术频谱接入问题。所提出的解决方案解决的一个相关应用是部署在未许可或轻度许可频谱中的物联网（IoT）设备的动态频谱接入，其中分布式IoT设备需要与其他活动系统共存。无感知频谱接入和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，大大提高频谱利用效率，并显着缓解有限的无线电频谱上不断增加的压力。该研究的跨学科性质自然会转化为案例研究和项目，在通信，机器学习和网络领域的PI教授的一些本科生和研究生水平的课程。该项目旨在开发一套基于在线学习的频谱接入算法，用于无传感设备与其他有源系统共存。第一个研究的重点是提高学习效率，通过引入最佳手臂识别框架，并提出元学习和良好的信道识别算法。第二个推力是致力于设计频谱接入机制，可以无缝集成混合自动重传请求（HARQ）。新的算法将被设计为学习用于可能的重传的信道的最佳序列，并且增强用于捕获HARQ的编码级行为的细粒度控制。最后一个研究方向是考虑多用户多技术共存的情况，发展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真实世界的数据集对算法和频谱接入方案进行全面验证。该奖项反映了NSF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。