WiFiUS: Collaborative Research: Sequential Inference and Learning for Agile Spectrum Use

WiFiUS:协作研究:敏捷频谱使用的顺序推理和学习

基本信息

  • 批准号:
    1660128
  • 负责人:
  • 金额:
    $ 8.6万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-09-01 至 2018-02-28
  • 项目状态:
    已结题

项目摘要

A key imperative to expanding future wireless services is to overcome the spectral crunch. At present, static allocation and rigid regulation lead to under utilization of available spectral resources. Flexible spectrum use aims at exploiting under-utilized spectrum. Available spectrum opportunities may be non-contiguous, scattered over a large bandwidth, and are available locally and for a limited period of time due to the highly dynamic nature of wireless transmissions. This fuels the need to understand how to discover, assess and utilize the time-frequency-location varying spectral resources efficiently and with minimal delay. Moreover, it is critical to access identified idle spectrum in an agile manner.This project will design sequential inference and learning algorithms for agile spectrum access when the state of the spectrum varies rapidly. The key advantage of sequential algorithms, as compared to block-wise algorithms, is that they typically lead to significantly reduced decision delays. The overarching goal of this project is to design sequential inference and learning algorithms for agile spectrum utilization. In particular, this project will employ advanced sequential inference and learning methods for the following three interconnected yet increasingly sophisticated and demanding tasks: 1) to employ sequential reinforcement learning and sequential inference algorithms to design sensing policies for rapid spectrum opportunities discovery; 2) to design sequential algorithms for fast and accurate spectrum quality assessment; and 3) to build, maintain and exploit an interference map of the area where our network operates and represent it as a spatial potential field. The proposed research is expected to make substantial contributions to both applications and theory. On the application level, the proposed research has the potential to substantially improve spectral efficiency by introducing novel tools from sequential analysis, machine learning and statistical inference for the design of spectrum discovery, assessment and exploitation policies. On the theoretical level, the proposed project will advance the state of the art in sequential analysis and contribute new approaches to the general methodological base for optimal stopping, control and machine learning problems. Furthermore, new methods and theory of modeling and exploiting knowledge of interference using spatial potential fields, sequential statistics and advanced propagation modeling will be developed.
扩展未来无线服务的关键是克服频谱紧缩。目前,静态分配和刚性监管导致可用频谱资源利用率不足。灵活的频谱使用旨在利用未充分利用的频谱。由于无线传输的高度动态性质,可用频谱机会可以是不连续的、分散在大带宽上的,并且在本地可用并且在有限的时间段内可用。这激发了理解如何有效地并且以最小延迟发现、评估和利用时频位置变化频谱资源的需要。此外,以敏捷的方式访问已识别的空闲频谱是至关重要的,本项目将设计顺序推理和学习算法,当频谱的状态快速变化的敏捷频谱访问。与块式算法相比,顺序算法的主要优点是它们通常会显著减少决策延迟。这个项目的首要目标是设计顺序推理和学习算法,灵活的频谱利用率。特别是,该项目将采用先进的顺序推理和学习方法来完成以下三个相互关联但日益复杂和苛刻的任务:1)采用顺序强化学习和顺序推理算法来设计用于快速频谱机会发现的感知策略; 2)设计用于快速和准确的频谱质量评估的顺序算法;以及3)建立、维护和利用我们的网络运行的区域的干扰图,并将其表示为空间势场。预计该研究将在应用和理论上做出实质性贡献。在应用层面上,拟议的研究有可能大大提高频谱效率,通过引入新的工具,从顺序分析,机器学习和统计推断的频谱发现,评估和开发政策的设计。在理论层面上,拟议的项目将推进序列分析的最新技术,并为最佳停止,控制和机器学习问题的一般方法基础提供新的方法。此外,将开发新的方法和理论,利用空间势场,顺序统计和先进的传播建模的干扰建模和利用知识。

项目成果

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会议论文数量(0)
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Lifeng Lai其他文献

Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk
具有条件风险价值的鲁棒风险敏感强化学习
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xinyi Ni;Lifeng Lai
  • 通讯作者:
    Lifeng Lai
NEW USES FOR OLD SMARTPHONES
旧智能手机的新用途
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Lifeng Lai;Michael Smith;Kewen Gu
  • 通讯作者:
    Kewen Gu
Minimax Optimal Q Learning with Nearest Neighbors
最近邻的 Minimax 最优 Q 学习
  • DOI:
    10.48550/arxiv.2308.01490
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Puning Zhao;Lifeng Lai
  • 通讯作者:
    Lifeng Lai
Key Generation using Ternary Tree based Group Key Generation for Data Encryption and Classification
使用基于三叉树的组密钥生成进行数据加密和分类的密钥生成
  • DOI:
    10.5120/ijca2017912883
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nikita Gupta;Amit Saxena;Maithili Narasimha;Randy Katz;Alfin Abraham;Lifeng Lai
  • 通讯作者:
    Lifeng Lai
Ultra-reliable and low-latency communications: applications, opportunities and challenges
  • DOI:
    10.1007/s11432-020-2852-1
  • 发表时间:
    2021-01-20
  • 期刊:
  • 影响因子:
    7.600
  • 作者:
    Daquan Feng;Lifeng Lai;Jingjing Luo;Yi Zhong;Canjian Zheng;Kai Ying
  • 通讯作者:
    Kai Ying

Lifeng Lai的其他文献

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{{ truncateString('Lifeng Lai', 18)}}的其他基金

CIF: Small: Adversarially Robust Reinforcement Learning: Attack, Defense, and Analysis
CIF:小型:对抗性鲁棒强化学习:攻击、防御和分析
  • 批准号:
    2232907
  • 财政年份:
    2023
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CIF: SMALL: kNN methods for functional estimation and machine learning
CIF:SMALL:用于功能估计和机器学习的 kNN 方法
  • 批准号:
    2112504
  • 财政年份:
    2021
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CCSS: Collaborative Research: Sketching for High Dimensional Data Analysis in IoT
CCSS:协作研究:物联网高维数据分析草图
  • 批准号:
    2000415
  • 财政年份:
    2020
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CIF: Small: Adversarially Robust Statistical Inference
CIF:小:对抗性稳健的统计推断
  • 批准号:
    1908258
  • 财政年份:
    2019
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CIF: Small: Distributed Statistical Inference with Compressed Data
CIF:小型:使用压缩数据进行分布式统计推断
  • 批准号:
    1717943
  • 财政年份:
    2017
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CCSS: Quickest Detection Under Energy Constraints
CCSS:能量限制下最快的检测
  • 批准号:
    1711468
  • 财政年份:
    2017
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CAREER: Building Secure Wireless Communication Systems via Physical Layer Resources
职业:通过物理层资源构建安全的无线通信系统
  • 批准号:
    1760889
  • 财政年份:
    2017
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Continuing Grant
CIF: Small: Collaborative Research: Secret Key Generation Under Resource Constraints
CIF:小型:协作研究:资源限制下的密钥生成
  • 批准号:
    1665073
  • 财政年份:
    2016
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CCSS: Collaborative Research: Developing A Physical-Channel Based Lightweight Authentication System for Wireless Body Area Networks
CCSS:协作研究:为无线体域网开发基于物理通道的轻量级身份验证系统
  • 批准号:
    1660140
  • 财政年份:
    2016
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant
CIF: Small: Collaborative Research: Secret Key Generation Under Resource Constraints
CIF:小型:协作研究:资源限制下的密钥生成
  • 批准号:
    1618017
  • 财政年份:
    2016
  • 资助金额:
    $ 8.6万
  • 项目类别:
    Standard Grant

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WiFiUS:协作研究:大规模位置感知异构物联网系统的可扩展边缘架构
  • 批准号:
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WiFiUS: Collaborative Research: Low Overhead Wireless Access Solutions for Massive and Dynamic IoT Connectivity
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  • 批准号:
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    2017
  • 资助金额:
    $ 8.6万
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WiFiUS:协作研究:物联网中的安全推理
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