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.
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