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:
  • 金额:
    $ 21.96万
  • 依托单位:
  • 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

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。

项目成果

期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Cascading Bandits with Two-Level Feedback
具有两级反馈的级联 Bandits
Optimizing Federated Averaging over Fading Channels
Federated Multi-armed Bandits with Personalization
  • DOI:
  • 发表时间:
    2021-02
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chengshuai Shi;Cong Shen;Jing Yang
  • 通讯作者:
    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
Reward Teaching for Federated Multiarmed Bandits
  • DOI:
    10.1109/tsp.2023.3333658
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang
  • 通讯作者:
    Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang
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Cong Shen其他文献

Stability analysis for interval time-varying delay systems based on time-varying bound integral method
基于时变界限积分法的区间时变时滞系统稳定性分析
  • DOI:
    10.1016/j.jfranklin.2014.07.015
  • 发表时间:
    2014-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Qian Wei;Li Tao;Cong Shen;Fei Shumin
  • 通讯作者:
    Fei Shumin
Stochastic Linear Contextual Bandits with Diverse Contexts
具有不同上下文的随机线性上下文强盗
Output-feedback stabilization control of systems with random switchings and state jumps
具有随机切换和状态跳跃的系统的输出反馈稳定控制
Multi-relation graph embedding for predicting miRNA-target gene interactions by integrating gene sequence information
通过整合基因序列信息预测 miRNA-靶基因相互作用的多关系图嵌入
On the Design of Modern Multilevel Coded Modulation for Unequal Error Protection
论现代多级编码调制的不等差错保护设计

Cong Shen的其他文献

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{{ truncateString('Cong Shen', 18)}}的其他基金

Collaborative Research: CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization
合作研究:CPS 媒介:空中学习:用于在线联合优化的跨层无人机编排
  • 批准号:
    2313110
  • 财政年份:
    2023
  • 资助金额:
    $ 21.96万
  • 项目类别:
    Standard Grant
CAREER: Towards a Communication Foundation for Distributed and Decentralized Machine Learning
职业:为分布式和去中心化机器学习建立通信基础
  • 批准号:
    2143559
  • 财政年份:
    2022
  • 资助金额:
    $ 21.96万
  • 项目类别:
    Continuing Grant
CCSS: Collaborative Research: Towards a Resource Rationing Framework for Wireless Federated Learning
CCSS:协作研究:无线联邦学习的资源配给框架
  • 批准号:
    2033671
  • 财政年份:
    2020
  • 资助金额:
    $ 21.96万
  • 项目类别:
    Standard Grant
Collaborative Research: MLWiNS: Dino-RL: A Domain Knowledge Enriched Reinforcement Learning Framework for Wireless Network Optimization
合作研究:MLWiNS:Dino-RL:用于无线网络优化的领域知识丰富的强化学习框架
  • 批准号:
    2002902
  • 财政年份:
    2020
  • 资助金额:
    $ 21.96万
  • 项目类别:
    Standard Grant

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