EAGER: SC2: SpeCOlab Spectrum Collaboration

可用于无线通信的无线电频谱是一种可重复使用但有限的资源。手机、计算机和物联网的需求呈指数级增长，推动了对显著改善动态频谱共享和分配的需求。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，该项目研究完全自动化频谱管理的方法，将学习如何协作共享频谱留给单独的设备。该项目团队是一个跨学科的研究小组，汇集了“大数据”、机器学习(ML)、电信和信号处理等学科。高效的机会式无线电频谱分配需要智能动态代理，能够在几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。要做出明智的频谱分配决策，一个关键因素是准确识别无线电信号，如无线电和电视广播、局域网和广域数据网络、蜂窝电话、模拟和数字语音、雷达等，这些信号受到噪声、衰落效应、定时误差和传输通道失真的污染。该项目的重点是制定和实施ML分类策略、无线网络配置策略和多网络协作策略。在已用于图像和语音识别的成功策略的基础上，该项目采用深度学习(DL)算法，例如卷积神经网络、深度神经网络和强化学习网络，用于通信信号的分类。

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.