EAGER: SC2: SpeCOlab Spectrum Collaboration

EAGER：SC2：SpeCOlab 频谱协作

基本信息

批准号：
1738097
负责人：
Dirk Grunwald
金额：
$ 10万
依托单位：
University of Colorado at Boulder
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：
2017-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738097&HistoricalAwards=false
关键词：
EAGER SC2 SpeCOlab Spectrum Collaboration

项目摘要

The radio spectrum that is usable for wireless communications is a reusable but finite resource. Exponentially growing demand by phones, computers, and the internet of things drives a need for significantly improved dynamic spectrum sharing and allocation. Spectrum allocation is slowly moving towards automated management, with improved efficiency and new wireless services enabled at each step. New spectrum systems still require controlled management of the spectrum. By comparison, this project investigates ways to completely automate the management of spectrum, leaving it to individual devices to learn how to collaboratively share spectrum.The project team is an interdisciplinary research group that brings together the disciplines of "Big Data", machine learning (ML), telecommunications, and signal processing. Efficient opportunistic radio spectrum allocation requires intelligent dynamic agents that can make real-time decisions at the time scale of tens of milliseconds, and can only be achieved using sophisticated machine learning algorithms. A key ingredient for making informed spectrum allocation decisions is the accurate identification of radio signals such as radio and TV broadcasting, local and wide area data networks, cell phones, analog and digital voice, radar, etc., which have been contaminated by noise, fading effects, timing errors, and transmission channel distortion. The project focus is on the development and implementation of ML strategies for classification, strategies for wireless network configuration and multi-network collaboration. Building upon successful strategies that have been used for image and voice recognition, this project adapts deep learning (DL) algorithms such as convolutional neural networks, deep neural networks, and reinforcement learning networks for the classification of communication signals.

可用于无线通信的无线电频谱是可重复使用但有限的资源。电话、计算机和物联网的需求呈指数级增长，推动了对动态频谱共享和分配的显着改进。频谱分配正在慢慢走向自动化管理，每一步都提高了效率并启用了新的无线服务。新的频谱系统仍然需要对频谱进行受控管理。相比之下，该项目研究了完全自动化频谱管理的方法，让各个设备学习如何协作共享频谱。该项目团队是一个跨学科研究小组，汇集了“大数据”、机器学习（ ML）、电信和信号处理。高效的机会性无线电频谱分配需要智能动态代理，能够在数十毫秒的时间尺度上做出实时决策，并且只能使用复杂的机器学习算法来实现。做出明智的频谱分配决策的一个关键因素是准确识别无线电信号，例如广播和电视广播、局域和广域数据网络、手机、模拟和数字语音、雷达等，这些信号已受到噪声、衰落效应、定时误差和传输信道失真。该项目的重点是开发和实施机器学习分类策略、无线网络配置策略和多网络协作策略。该项目以图像和语音识别中使用的成功策略为基础，采用卷积神经网络、深度神经网络和强化学习网络等深度学习 (DL) 算法来对通信信号进行分类。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

This is Your President Speaking: Spoofing Alerts in 4G LTE Networks

这是你们的总统在讲话：4G LTE 网络中的欺骗警报

DOI：
10.1145/3307334.3326082
发表时间：
2019-06
期刊：
and Services
影响因子：
0
作者：
Lee, Gyuhong;Lee, Jihoon;Lee, Jinsung;Im, Youngbin;Hollingsworth, Ma;Wustrow, Eric;Grunwald, Dirk;Ha, Sangtae
通讯作者：
Ha, Sangtae

CASTLE over the Air -- Distributed Scheduling for Cellular Data Transmissions (demo)

CASTLE over the Air——蜂窝数据传输的分布式调度（演示）

DOI：
10.1145/3307334.3328576
发表时间：
2019-06
期刊：
and Services
影响因子：
0
作者：
Dhawaskar Sathyanarayana, Sandesh;Lee, Jihoon;Lee, Jinsung;Im, Youngbin;Rahimzadeh, Parisa;Zhang, Xiaoxi;Hollingsworth, Ma;Joe;Grunwald, Dirk;Ha, Sangtae
通讯作者：
Ha, Sangtae