Collaborative Research: NeTS: Medium: Towards High-Performing LoRa with Embedded Intelligence on the Edge

协作研究：NeTS：中：利用边缘嵌入式智能实现高性能 LoRa

• 批准号: 2312676
• 负责人: Shigang Chen
• 金额: $ 28.22万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312676&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; Medium; Towards

LoRa (short for Long Range), a spread-spectrum modulation technique, has emerged in recent years as a promising mechanism to connect billions of low-cost Internet of Things (IoT) devices for wide-area applications such as smart metering, environment monitoring, and logistic tracking. However, current LoRa networks have been observed to have shorter coverage range, lower energy efficiency, and higher deployment cost than originally promised. The fundamental problem is that current LoRa receivers perform poorly when there is complex environmental noise. This project uses the feature extraction capability of modern deep neural networks (DNN) and the computational resources now available on edge devices to create better performing LoRa networks. The success of this project will reduce the cost of deploying and maintaining real-world LoRa networks, and thus will accelerate adoption of wide-area IoT applications which will enhance efficiency of smart cities and other verticals. The project also develops curricular materials for applying machine learning to wireless networking in both undergraduate and graduate programs. This project offers research training opportunities to underrepresented students from diverse groups and age levels. This project designs a new LoRa physical layer to enhance long-distance and low-power LoRa communication. The project includes three parts. (1) Design of a multi-dimension multi-resolution neural-enhanced LoRa decoder that can be used with standard LoRa transmissions in a single-gateway setting. The new decoder improves performance by capturing and processing multi-dimensional features of standard LoRa signals even when the signal strength is far below the noise floor; (2) Co-design of a neural-enhanced encoder-decoder pair for use in a single-gateway setting. The encoder creates a non-standard LoRa transmission that provides a much richer feature space for neural-enhanced decoding and thus further enhances performance in high-noise situations. (3) Co-design a neural-enhanced multi-gateway symbol decoder and a frequency-aware encoder for use in a multi-gateway setting. The design uses the spatial diversity of multiple gateways to enhance the SNR (signal to noise ratio) of the received signals even further. To evaluate the proposed techniques, this project uses hardware-software co-design to develop an end-to-end DNN-empowered LoRa prototype. The code and data generated in the project are available to the research community for further investigation.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.

LoRa（Long Range的缩写）是一种扩频调制技术，近年来已成为连接数十亿低成本物联网（IoT）设备的一种有前途的机制，用于智能计量，环境监测和物流跟踪等广域应用。然而，据观察，目前的LoRa网络的覆盖范围较短，能源效率较低，部署成本高于最初的承诺。根本问题是，当前的LoRa接收器在存在复杂环境噪声时表现不佳。该项目使用现代深度神经网络（DNN）的特征提取能力和边缘设备上现有的计算资源来创建性能更好的LoRa网络。该项目的成功将降低部署和维护现实世界LoRa网络的成本，从而加速广域物联网应用的采用，从而提高智慧城市和其他垂直行业的效率。该项目还开发了在本科和研究生课程中将机器学习应用于无线网络的课程材料。该项目为来自不同群体和年龄层次的代表性不足的学生提供研究培训机会。该项目设计了一个新的LoRa物理层，以增强长距离和低功耗的LoRa通信。该项目包括三个部分。(1)设计多维多分辨率神经增强LoRa解码器，可在单网关设置中与标准LoRa传输一起使用。新的解码器通过捕获和处理标准LoRa信号的多维特征来提高性能，即使信号强度远低于本底噪声;（2）共同设计用于单网关设置的神经增强型编码器-解码器对。编码器创建了一个非标准的LoRa传输，为神经增强解码提供了更丰富的特征空间，从而进一步增强了高噪声情况下的性能。(3)共同设计一个神经增强型多网关符号解码器和一个频率感知编码器，用于多网关设置。该设计使用多个网关的空间分集来进一步增强接收信号的SNR（信噪比）。为了评估所提出的技术，该项目使用硬件-软件协同设计来开发端到端DNN授权的LoRa原型。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。