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A Deep Reinforcement Learning Approach for Automated Chamber Configuration Replicating mmWave Directional Industrial Channel Behavior

Published

Author(s)

Mohamed Hany, Sudantha Perera, Carnot Nogueira, Rick Candell, Kate Remley, Matt Simons

Abstract

Industrial wireless channels have different characteristics than home and office channels due to their reflective nature. Moreover, the millimeter-wave (mmWave) wireless bands can play a big role in improving industrial wireless systems due to their large available bandwidth and the short wavelength that allows a large number of antennas to be located closely to each other. Wireless test chambers are used for over-the-air (OTA) testing and assessment of various protocols and equipment. However, in order to closely characterize a system under test, the chamber should be configured to replicate the environment where the system is deployed. In this work, we present a deep reinforcement learning protocol to configure a test chamber in order to replicate the spatial characteristics of measured mmWave channels in industrial environments. The proposed algorithm is general for any N-dimensional chamber configurations where it can be used to configure various reflectors, absorbers, and paddles inside a wireless test chamber.
Proceedings Title
The 100th Automatic Radio Frequency Techniques Group (ARFTG) Microwave Measurement Conference
Conference Dates
January 22-25, 2023
Conference Location
Las Vegas, NV, US

Keywords

Over-the-air test chamber, automatic configuration, Channel modeling, industrial wireless, deep reinforcement learning, wireless systems

Citation

Hany, M. , Perera, S. , Nogueira, C. , Candell, R. , Remley, K. and Simons, M. (2023), A Deep Reinforcement Learning Approach for Automated Chamber Configuration Replicating mmWave Directional Industrial Channel Behavior, The 100th Automatic Radio Frequency Techniques Group (ARFTG) Microwave Measurement Conference, Las Vegas, NV, US, [online], https://doi.org/10.1109/ARFTG56062.2023.10148878, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935810 (Accessed May 21, 2024)

Issues

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Created January 25, 2023, Updated October 24, 2023