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CIF:Small: A Tensor-based Framework for Reliable Radio Cartography

CIF:Small：基于张量的可靠无线电制图框架

基本信息

批准号：
1718195
负责人：
Nazanin Rahnavard
金额：
$ 39.53万
依托单位：
The University of Central Florida Board of Trustees
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718195&HistoricalAwards=false
关键词：
CIF Small Tensor based Framework

项目摘要

The goal of this project is to devise an efficient framework for achieving radio frequency (RF) spectrum awareness through generating reliable and dynamic radio environment maps (REMs). This is a notable step towards the proliferation of realistic cognitive radio networks (CRNs), which enable spectrum sharing to alleviate the problem of spectrum scarcity. This efficient use of spectrum empowers ever-increasing applications and services with a great impact on national health, welfare, public safety, and economic growth. In addition, the developed data analysis tools provide a distinctive solution to high-dimensional signal sampling and processing, enabling further development in a wide range of applications, such as big data, Internet of Things, and wireless sensor networks, which can promote social and economic progress. This project integrates research and education through new course development and revisions, and involving underrepresented minorities, graduate, and undergraduate students in research.This research bridges the gap between the theoretical research in tensor data analysis and wireless communications and networking to facilitate spectrum awareness. The investigators will develop a tensor-based framework to generate dynamic and reliable REMs that include RF signal power distribution over space, time and frequency. Tensor-based analysis facilitates the integration of the inherent properties and data structures that exist in CRNs as well as the prior knowledge of the behavior of the primary network. Bayesian and structure-based tensor decomposition is investigated, for which existence and uniqueness conditions are studied. The outcome of the decomposition is employed to obtain a model-based interpolation of sensor readings and to generate the REM. Corresponding to an REM, a reliability map will be created and utilized for optimal joint spectrum sensor and channel selection.

该项目的目标是设计一个有效的框架，通过生成可靠和动态的无线电环境地图（REM）来实现射频（RF）频谱感知。这是朝着现实认知无线电网络（CRN）的扩散迈出的重要一步，CRN使频谱共享能够缓解频谱稀缺问题。频谱的这种有效利用为不断增加的应用和服务提供了支持，对国家健康、福利、公共安全和经济增长产生了重大影响。此外，开发的数据分析工具为高维信号采样和处理提供了独特的解决方案，使其在大数据、物联网和无线传感器网络等广泛应用中得到进一步发展，可以促进社会和经济的进步。本项目通过新课程的开发和修订，将研究与教育相结合，并让少数民族、研究生和本科生参与研究，弥合张量数据分析理论研究与无线通信和网络之间的差距，以促进频谱感知。研究人员将开发一个基于张量的框架，以生成动态和可靠的REM，其中包括空间，时间和频率上的RF信号功率分布。基于张量的分析有助于整合CRN中存在的固有属性和数据结构以及主网络行为的先验知识。研究了贝叶斯和基于结构的张量分解，研究了其存在唯一性条件。分解的结果用于获得传感器读数的基于模型的插值并生成REM。对应于REM，将创建可靠性图并将其用于最佳联合频谱传感器和信道选择。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Dynamic Sensor Selection for Reliable Spectrum Sensing via E-Optimal Criterion

通过 E-Optimal Criterion 实现可靠频谱传感的动态传感器选择

DOI：
10.1109/mass.2017.72
发表时间：
2017
期刊：
IEEE Mobile and Ad Hoc Sensor Systems (MASS
影响因子：
0
作者：
Joneidi, Mohsen;Zaeemzadeh, Alireza;Rahnavard, Nazanin
通讯作者：
Rahnavard, Nazanin

A Framework for Clustered and Skewed Sparse Signal Recovery

DOI：
10.1109/tsp.2018.2839622
发表时间：
2018-08
期刊：
IEEE Transactions on Signal Processing
影响因子：
5.4
作者：
Sheng Wang;Nazanin Rahnavard
通讯作者：
Sheng Wang;Nazanin Rahnavard

EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks

使用张量分解和深度卷积神经网络进行脑电图信号降维和分类

DOI：
10.1109/mlsp.2019.8918754
发表时间：
2019
期刊：
IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP
影响因子：
0
作者：
Taherisadr, M;Joneidi, M;Rahnavard, N.
通讯作者：
Rahnavard, N.

Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

DOI：
10.1109/mass.2017.50
发表时间：
2017-10
期刊：
2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
影响因子：
0
作者：
Alireza Zaeemzadeh;M. Joneidi;Behzad Shahrasbi;Nazanin Rahnavard
通讯作者：
Alireza Zaeemzadeh;M. Joneidi;Behzad Shahrasbi;Nazanin Rahnavard

MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI