Wideband Cyclostationary Spectrum Analysis for Smart Factory Wireless Channels

Smart and highly automated factories rely on time sensitive networks (TSNs) to coordinate the actions of robots working together to complete a task. The dense layout of metallic objects on the factory floor creates a complex electromagnetic environment with long-duration multipath. Furthermore, the motion of the robotic workers and the use of multi-carrier waveforms, such as Orthogonal Frequency Division Multiplexing (OFDM), limit the coherence time of the channel. In this paper, we propose a technique for characterizing the frequency behavior of wireless channels in industrial settings. Our proposed algorithm relies on detecting cyclostationary spectral features and includes a novel method for combining the frequency bins of a spectrogram. Cyclostationary feature detection is especially useful for integrating a sensing capability and the communication function of a wireless network. Synthetic aperture sounding measurements of the wireless channel within a utility plant are used to evaluate the proposed algorithm.