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: 2312675
• 负责人: Mi Zhang
• 金额: $ 45万
• 依托单位: Ohio State University
• 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=2312675&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.

近年来，洛拉（远距离远距离远距离）已成为一种有前途的机制，可以连接数十亿个低成本互联网（IoT）设备，用于智能计量，环境监测和物流跟踪等广域应用程序。但是，已经观察到当前的洛拉网络的覆盖范围较短，能源效率较低，而部署成本较高。基本问题是，当存在复杂的环境噪声时，当前的洛拉接收器的性能很差。该项目使用现代深神经网络（DNN）的功能提取能力和Edge设备上现在可用的计算资源来创建更好的性能LORA网络。该项目的成功将降低部署和维护现实世界的洛拉网络的成本，因此将加速采用广阔的物联网应用程序，从而提高智能城市和其他垂直行业的效率。该项目还开发了用于将机器学习应用于本科和研究生课程中的无线网络的课程材料。该项目为来自不同群体和年龄水平的人数不足的学生提供了研究培训机会。该项目设计了一个新的Lora物理层，以增强长距离和低功率Lora通信。该项目包括三个部分。 （1）设计多维的多分辨率神经增强的Lora解码器，该解码器可与单扇门设置中的标准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噪声底部。 （2）在单门设置中使用神经增强的编码码对编码对的共同设计。编码器创建了一个非标准的Lora变速器，为神经增强解码提供了更丰富的功能空间，从而进一步提高了高噪声情况下的性能。 （3）共同设计神经增强的多门符号解码器和用于多门设置的频率感知编码器。该设计使用多个网关的空间多样性来进一步增强接收信号的SNR（信号与噪声比）。为了评估所提出的技术，该项目使用硬件软件共同设计来开发端到端DNN授权的Lora原型。该项目中生成的代码和数据可供研究社区进行进一步调查。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准的评估来支持的。