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- W4383219106 abstract "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." @default.
- W4383219106 created "2023-07-06" @default.
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- W4383219106 date "2023-06-11" @default.
- W4383219106 modified "2023-09-26" @default.
- W4383219106 title "Wideband Cyclostationary Spectrum Analysis for Smart Factory Wireless Channels" @default.
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- W4383219106 doi "https://doi.org/10.1109/dsp58604.2023.10167932" @default.
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