EAGER: SC2: PHY-Layer-Integrated Collaborative Learning in Spectrum Coordination

EAGER：SC2：频谱协调中的 PHY 层集成协作学习

• 批准号: 1737842
• 负责人: Sebastian Pokutta
• 金额: $ 9.99万
• 依托单位: Georgia Tech Research Corporation
• 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: 2017-03-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737842&HistoricalAwards=false
• 关键词: EAGER; SC2; PHY; Layer; Integrated

With the explosion of wireless devices, spectrum is becoming a scarce resource that wireless systems fiercely compete for. To ensure that future civilian and military systems, ranging from connected Internet of Things (IoT) devices to battlefield ad-hoc networks, continue to support services with growing quality, systems must evolve beyond the traditional spectrum licensing model and towards an intelligent spectrum sharing paradigm, in which networks nodes collaborate to efficiently share the spectrum. This radical paradigm shift requires the integration of the latest machine learning advances with the more recent progress in software defined radio, in order to endow wireless devices with the intelligence and agility necessary to realize the vision of efficient unsupervised spectrum sharing. The proposed research aims at addressing this fundamental issue, and will offer ample opportunities to provide interdisciplinary training of students at the intersection of machine learning and communications engineering.The project envisions a paradigm shift in radio design, which will intertwine agile communications engineering techniques with advanced machine learning algorithms to fuse the traditional physical-layer and link layers into a "collaboration layer". Specifically, the approach comprises three key elements: (1) a multi-carrier modulation format at the physical layer that provides the required agility to react to interfering signals; (2) a high-performing modulation recognition software that exploits the latest advances in deep learning and convolutional neural networks to accurately classify the radio frequency signals in the environment; and (3) a decision module exploiting the latest advances in regret minimization online algorithms to achieve high exploration versus exploitation performance in the wireless environment.

随着无线设备的爆炸式增长，频谱正成为无线系统激烈争夺的稀缺资源。为了确保未来的民用和军用系统（从联网的物联网 (IoT) 设备到战场自组织网络）继续支持质量不断提高的服务，系统必须超越传统的频谱许可模式，转向智能频谱共享范例，其中网络节点协作以有效地共享频谱。这种根本性的范式转变需要将最新的机器学习进展与软件定义无线电的最新进展相结合，以便赋予无线设备实现高效无监督频谱共享愿景所需的智能和敏捷性。拟议的研究旨在解决这一基本问题，并将提供充足的机会，为学生提供机器学习和通信工程交叉领域的跨学科培训。该项目设想了无线电设计的范式转变，它将敏捷的通信工程技术与先进的技术结合在一起。机器学习算法将传统的物理层和链路层融合为“协作层”。具体来说，该方法包括三个关键要素：（1）物理层的多载波调制格式，提供对干扰信号做出反应所需的灵活性； (2) 高性能调制识别软件，利用深度学习和卷积神经网络的最新进展，对环境中的射频信号进行准确分类； (3) 决策模块利用遗憾最小化在线算法的最新进展，在无线环境中实现较高的探索与利用性能。