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)算法的能力，以高频谱效率和低功耗进行无线通信。这些技术将产生高级网络协议，通过安全地识别在没有(或使用最少)控制信令的情况下在周围环境中活动的设备，从而消耗最少的资源。除了了解通道使用情况和设备活动外，DISCOVER还将允许在硬件中快速部署这些算法，以便进行实时推断。因此，Discovery与美国总统2019年2月发布的行政命令直接一致，该行政命令旨在将美国人工智能(AI)能力的研发放在优先位置。Discovery旨在通过协作研讨会将业界、学术界和政府利益相关者聚集在一起，以确定高优先级挑战、可用数据源的限制，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。信号数据集和模拟代码的开源发布将促进无线研究人员与核心机器学习领域专家的新交互。DISCOVER有三个目标，即通过使用深度学习架构来优化频谱利用，同时兼顾节能或对身份欺骗的弹性：1.它旨在探索深度卷积神经网络(CNN)架构，该架构将允许高精度的设备分类，并演示如何消除与标识相关的协议字段。这种减少数据包头的方法将实现可量化的频谱利用率提升，特别是对于大规模部署物联网(IoT)。2.它旨在演示第一个环路学习射频(RF)系统，其中频谱驱动的决策通过直接在设备硬件上实施的实时深度学习算法来实现。这将显著节省嵌入式物联网设备的能源。3.项目中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，它还将创建社区射频信号数据集，以确保为更大的研究社区提供标准化验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.