EAGER: SC2: SpeCOlab Spectrum Collaboration

EAGER：SC2：SpeCOlab 频谱协作

• 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

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增长，这驱动了需要显着改善动态频谱共享和分配的需求。频谱分配正在缓慢地朝着自动化管理方向发展，并在每个步骤中启用了新的无线服务。新的频谱系统仍然需要对频谱进行控制的管理。相比之下，该项目调查了完全自动化频谱管理的方法，并将其留给各个设备，以学习如何协作分享频谱。项目团队是一个跨学科研究小组，将“大数据”，机器学习（ML），电信和信号处理的学科汇集在一起​​。有效的机会无线电频谱分配需要智能动态代理，这些动态代理可以在数十毫秒的时间范围内做出实时决策，并且只能使用复杂的机器学习算法来实现。做出知情频谱分配决策的关键要素是准确识别无线电信号，例如无线电和电视广播，本地和广泛的数据网络，手机，模拟和数字语音，雷达，雷达等，这些信号已被噪声，淡出效果，时机效果，时机和传输通道扭曲所污染。 该项目的重点是用于制定和实施ML分类策略，无线网络配置策略和多网络协作。该项目以用于图像和语音识别的成功策略为基础，适应了深度学习（DL）算法，例如卷积神经网络，深神经网络和强化学习网络，以分类通信信号。

项目成果

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
• 期刊: and Services
• 作者: Dhawaskar Sathyanarayana, Sandesh;Lee, Jihoon;Lee, Jinsung;Im, Youngbin;Rahimzadeh, Parisa;Zhang, Xiaoxi;Hollingsworth, Max;Joe-Wong, Carlee;Grunwald, Dirk;Ha, Sangtae
• 通讯作者: Ha, Sangtae

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
• 期刊: and Services
• 作者: Lee, Gyuhong;Lee, Jihoon;Lee, Jinsung;Im, Youngbin;Hollingsworth, Max;Wustrow, Eric;Grunwald, Dirk;Ha, Sangtae
• 通讯作者: Ha, Sangtae