NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
A Deep Learning Framework for Industrial Wireless Networks
Published
Author(s)
Mohamed Hany, Rick Candell
Abstract
In this report, we introduce a framework to analyze, monitor, and identify the state of industrial wireless networks and their impact on industrial use cases. This framework is based on a deep learning approach for modeling the interactions between the wireless network and industrial use cases. The framework uses the information from different system layers including the spectrum measurements, physical layer metrics, network layer packets, and application layer production-related metrics in order to study the industrial wireless network behavior. The output of the framework can be generally used to improve system management and optimization functions.
Hany, M.
and Candell, R.
(2023),
A Deep Learning Framework for Industrial Wireless Networks, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2257, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956211
(Accessed October 13, 2025)