Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

Abstract

In this paper, we performed a series of measurement campaigns on the wireless electromagnetic noise for two typical industrial welding scenarios. On this basis, we investigate the characterization of the impulsive noise mainly from two aspects, that is, frequency and time domains. To start with, a novel denoising method based on the dynamic threshold is proposed to identify the desirable impulse noise from the background noise. Then, in the frequency domain, we focus on the power distribution of impulsive noise at different frequency bands. Results exhibit a shadow effect with regard to different frequency bands and we characterize it by using a linear function with a Gaussian distribution. Besides, analyses on the power spectrum correlation for different polarization modes and scenarios are also provided. In the time domain, we performed a series of statistical analyses from aspects of pulse amplitude, duration, and elapse interval to characterize the impulsive noise. Furthermore, three empirical distributions are employed to depict the parameters' variation tendency, that is, Cauchy distribution for amplitude, Gamma distribution for pulse duration, and exponential distribution for pulse interval. Finally, a firstorder two-states Markov method is proposed to model the industrial noise. Simulation results are proved to be

Adding realistic noise models to synthetic ground‐penetrating radar data

“…For example, Zhang et al. (2021) report decreasing noise power with increasing frequency and Breton et al. (2019) have observed that noise may occur in spatial noise clusters with spatial dimensions ranging from tens to hundreds of metres and with temporal variations of hours to weeks.…”

“…Studies investigating the statistical properties of radiofrequency noise in the GPR frequency band (10-1000 MHz) and different environments have shown that ambient EM noise can exhibit complex characteristics. For example, Zhang et al (2021) report decreasing noise power with increasing frequency and Breton et al (2019) have observed that noise may occur in spatial noise clusters with spatial dimensions ranging from tens to hundreds of metres and with temporal variations of hours to weeks. Furthermore, such noise often originates from a variety of sources and, thus, is difficult to be described as white Gaussian noise (Palaios et al, 2016).…”

GNSS and Massive MIMO : Spoofing, Jamming and Robust Receiver Design for Impulsive Noise

“…However, in reality, the noise may be non-Gaussian. For example, in [39], it is shown that the noises can be non-Gaussian, more specifically impulsive, and they can be modeled by using Gamma or Cauchy distributions based on some measurements. Actually, it is well known that the Gaussian receiver works poorly when the noise is heavy-tailed so there exist some robust estimation techniques such as M −estimator in the literature [40].…”