SpecEES: DISCOVER: Device Identification for Spectrum-optimization using COnVolutional nEural netwoRks

这个名为DISCOVER的项目的研究目标是利用深度机器学习（ML）算法的力量，以高频谱效率和低功耗进行无线通信。这些技术将产生先进的网络协议，该协议通过安全地识别在周围环境中活动的设备而无需（或具有最少的）控制信令来消耗最少的资源。除了学习信道使用和设备活动，DISCOVER还将允许在硬件中快速部署这些算法，以便进行实时推理。因此，DISCOVER直接与美国总统2019年2月的行政命令“保持美国在人工智能领域的领导地位”保持一致，该命令旨在优先研究和开发美国的人工智能（AI）能力。DISCOVER旨在通过协作研讨会将行业、学术界和政府利益相关者聚集在一起，以确定高优先级的挑战、可用数据源的局限性，并确定将塑造下一代无线技术的候选机器学习解决方案列表。信号数据集和仿真代码的开源版本将促进无线研究人员与核心机器学习领域专家的新互动。DISCOVER有三个目标，通过使用深度学习架构优化频谱利用率，同时兼顾节能或身份欺骗的弹性：1.它旨在探索深度卷积神经网络（CNN）架构，该架构将允许高度准确的设备分类，并演示如何消除与标识符相关的协议字段。这种减少数据包报头的方法将实现可量化的频谱利用率改善，特别是对于物联网（IoT）的大规模部署。2.它旨在展示第一个学习在环射频（RF）系统，其中通过直接在设备硬件上实现的实时深度学习算法实现频谱驱动决策。这将为嵌入式物联网设备节省大量能源。3.该项目开发的仿真引擎将使用户能够创建自定义信号来训练ML算法。此外，它将创建社区RF信号数据集，以确保更大的研究社区的标准化验证手段。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The research objective of this project, called DISCOVER, is to harness the power of deep machine learning (ML) algorithms to communicate wirelessly with high spectral efficiency and low power consumption. The techniques will result in advanced networking protocols that consume minimal resources by securely identifying the devices that are active in the surrounding environment without (or with minimal) control signaling. Apart from learning channel usage and device activity, DISCOVER will allow for rapidly deploying these algorithms in hardware, so that real-time inferences can be made. Thus, DISCOVER is directly aligned with the US President's executive order from February 2019 'Maintaining American Leadership in Artificial Intelligence' that seeks to prioritize research and development of America's artificial intelligence (AI) capabilities. DISCOVER aims to bring together industry, academia and government stakeholders through collaborative workshops towards identifying high priority challenges, limitations of available data sources, and identify a list of candidate machine learning solutions that will shape the next generation of wireless technologies. The open source release of signal datasets and simulation code will foster new interactions of wireless researchers with core machine learning domain experts.DISCOVER has three goals for optimizing spectrum utilization with overlapping interests of either energy saving or resilience to identity spoofing through the use of deep learning architectures: 1. It aims to explore deep convolutional neural network (CNN) architectures that will allow highly accurate device classification and demonstrate how to eliminate identifier-related protocol fields. This approach of reducing packet headers will achieve quantifiable spectrum utilization improvements, especially for large-scale deployment of the Internet of Things (IoT). 2. It aims to demonstrate the first learning-in-the-loop radio frequency (RF) system where spectrum-driven decisions are enabled through real-time deep learning algorithms implemented directly on the device hardware. This will result in significant energy savings for embedded IoT devices. 3. The emulation engine developed in the project will empower users to create custom-signals to train ML algorithms. Furthermore, it will create community RF signal datasets that will ensure means of standardized validation for the larger research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.