Tutorial: Hidden Outlier Interference and Its Mitigation

  • Room: Granby D
Tuesday, November 12, 2019: 9:00 AM - 12:00 PM


Speaker (confirmed)
Ruslan L. Davidchack, Ph.D.
Professor of Mathematical Modelling and Computation, Department of Mathematics
University of Leicester
Speaker (confirmed)
Alexei V. Nikitin, Ph.D.
Co-Founder and Chief Science Officer
Nonlinear LLC


Approved for 3 CompTIA CEUs: A+, Network+

In addition to ever-present thermal noise, various communication and sensor systems can be affected by interfering signals that originate from a multitude of other natural and technogenic (man-made) phenomena. Such interfering signals often have intrinsic "outlier" temporal and/or amplitude structures, which are different from the Gaussian structure of the thermal noise. The presence of different types of such outlier noise is widely acknowledged in multiple applications, under various general and application-specific names, most commonly as impulsive, transient, burst, or crackling noise. For example, outlier electromagnetic interference (EMI) is inherent in digital electronics and communication systems, transmitted into a system in various ways, including electrostatic coupling, electromagnetic induction, or RF radiation. However, although the detrimental effects of EMI are broadly acknowledged in the industry, its outlier nature often remains indistinct, and its omnipresence and impact, and thus the potential for its mitigation, remain greatly underutilized.

There are two fundamental reasons why the outlier nature of many technogenic interference sources is often dismissed as irrelevant. The first one is a simple lack of motivation. Without using nonlinear filtering techniques the resulting signal quality is largely invariant to a particular time-amplitude makeup of the interfering signal and depends mainly on the total power and the spectral composition of the interference in the passband of interest. Thus, unless the interference results in obvious, clearly identifiable outliers in the signal's band, the "hidden" outlier noise does not attract attention. The second reason is the highly elusive nature of outlier noise, and the inadequacy of tools used for its consistent observation and/or quantification. More important, the amplitude distribution of a non-Gaussian signal is generally modifiable by linear filtering, and such filtering can often convert the signal from sub-Gaussian into super-Gaussian, and vice versa. Thus apparent outliers in a signal can disappear and reappear due to various filtering effects, as the signal propagates through media and/or the signal processing chain.

This tutorial provides a concise overview of the methodology and tools, including their analog and digital implementations, for real-time mitigation of outlier interference in general and "hidden" wideband outlier noise in particular. Such mitigation is performed as a "first line of defense" against interference ahead of, or in the process of, reducing the bandwidth to that of the signal of interest. Either used by itself, or in combination with subsequent interference mitigation techniques, this approach provides interference mitigation levels otherwise unattainable, with the effects, depending on particular interference scenarios, ranging from "no harm" to spectacular. Although the main focus of this filtering technique is mitigation of wideband outlier noise affecting a band-limited signal of interest, it can also be used, given some a priori knowledge of the signal of interest's structure, to reduce outlier interference that is confined to the signal's band.


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Approved for 3 CompTIA CEUs: A+, Network+