Wideband Cyclostationary Spectrum Analysis for Smart Factory Wireless Channels
Peter Vouras, Mohamed Hany, Rick Candell
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.
2023 24th International Conference on Digital Signal Processing
, Hany, M.
and Candell, R.
Wideband Cyclostationary Spectrum Analysis for Smart Factory Wireless Channels, 2023 24th International Conference on Digital Signal Processing, Island of Rhodes , GR, [online], https://doi.org/10.1109/DSP58604.2023.10167932, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936906
(Accessed September 22, 2023)