AI-Assisted Configuration of Reverberation Chambers for Precision RF Environments Emulation

Industrial wireless channels differ significantly from home and office channels due to factors like larger distances, existence of heavy machinery, and composition of surroundings. Industrial environments can vary significantly in their physical characteristics including the size, surrounding objects, and construction materials. Wireless test chambers are used for over-the-air (OTA) testing and assessment of various protocols and equipment. However, to accurately characterize a system under test, a test chamber should be configured to replicate the environment where the system is deployed. In this work, we present a supervised learning protocol to configure a reverberation chamber in order to emulate the characteristics of measured wireless channels in industrial environments. The proposed algorithm is general for chamber configuration where it can be used to configure various reflectors, absorbers, and paddles inside a wireless test chamber. To realize this approach, a neural network is trained to generate a reverberation chamber configuration based on an input vector that includes wireless channel features such as channel gain, mean delay, delay spread, and K-factor.