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

• 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

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.

该项目为分布式低成本设备开发了一个新型的基于在线学习的框架，以有效地有效地访问共享频谱，而无需光谱传感。它特别着眼于没有强大的射频（RF）组件来实现宽带频谱感测的无敏感设备，并在分散的设置中解决了跨技术频谱访问问题。一个相关的应用程序提出的解决方案地址是部署在未经许可或轻度许可的频谱中的动态频谱访问（IoT）设备，其中分布式IoT设备需要与其他活动系统共存。无敏感范围的访问和共享框架有可能彻底改变现代和未来的无线网络的运行和管理，从而大大提高了频谱利用率，并大大减轻了对有限无线电频谱的不断增加的压力。该研究的跨学科性质自然会将PIS在通信，机器学习和网络领域教授的许多本科和研究生水平课程中转化为案例研究和项目。该项目旨在开发一系列基于在线学习的频谱访问算法，以与其他有效的系统共处。第一项研究的重点是通过引入最佳的ARM识别框架并提出元学习和良好的频道识别算法来提高学习效率。第二个推力致力于设计可以无缝整合混合自动重复请求（HARQ）的光谱访问机制。新颖的算法将旨在学习可能重新召集的通道的最佳序列，并增强了捕获HARQ的编码水平行为的细粒控制。调查的最后一个线程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访问算法。最后，将使用实验室测试床和现实世界数据集对算法和频谱访问方案进行彻底验证。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持。

项目成果

Cascading Bandits with Two-Level Feedback

具有两级反馈的级联 Bandits

• DOI: 10.1109/isit50566.2022.9834892
• 发表时间: 2022
• 期刊: 2022 IEEE International Symposium on Information Theory (ISIT
• 作者: Cheng, Duo;Huang, Ruiquan;Shen, Cong;Yang, Jing
• 通讯作者: Yang, Jing

Optimizing Federated Averaging over Fading Channels

• DOI: 10.1109/isit50566.2022.9834609
• 发表时间: 2022-06
• 期刊: 2022 IEEE International Symposium on Information Theory (ISIT)
• 作者: Yujia Mu;Cong Shen;Yonina C. Eldar

Reward Teaching for Federated Multiarmed Bandits

• DOI: 10.1109/tsp.2023.3333658
• 发表时间: 2023
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang

Federated Multi-armed Bandits with Personalization

• 发表时间: 2021-02
• 期刊: ArXiv
• 作者: Chengshuai Shi;Cong Shen;Jing Yang

On High-dimensional and Low-rank Tensor Bandits

关于高维低阶张量老虎机

• 发表时间: 2023
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT
• 作者: Shi, C.;Shen, C.;Sidiropoulos. N. D.
• 通讯作者: Sidiropoulos. N. D.