Statistical Analysis and Modeling of LowFrequency Radio Noise and
Optimization of LowFrequency Communications
Douglas Chrissan
Department of Electrical Engineering
Abstract
Naturally occurring radio noise above approximately 100 MHz is well modeled for most applications as a Gaussian random process. Below 100
MHz, however, radio noise is impulsive in nature and is not well modeled as Gaussian. Individual atmospheric events (mainly "sferics", the
electromagnetic emissions from lightning) produce large noise impulses, so power is not spread evenly in time but instead depends on the
occurrence of discrete, clustered events. Due to large variations in the occurrence of sferics on a seasonal and diurnal basis (and with the
passing of individual storms), atmospheric noise is nonstationary.
The objective of this work is the statistical characterization and modeling of atmospheric radio noise in the range 10 Hz  60 kHz (denoted
"lowfrequency" noise), with the specific goal of improving communication systems operating in this range. The statistics analyzed include
seasonal and diurnal variations, amplitude probability distributions (APD's), impulse interarrival time distributions, impulse counting statistics,
background noise statistics, and power spectral densities. The analyses are based on many thousands of hours of noise measurements made
by the Stanford Radio Noise Survey System.
Based on the statistical characteristics of the noise data, a new clustering Poisson atmospheric noise model is developed. This model is based
on several previously known statisticalphysical models, but in addition it takes into account the clustering aspects of sferics. It is verified that the
clustering model accurately predicts the statistical features found in the data.
Lowfrequency noise is shown to have an alphastable noise envelope distribution for locations removed from heavy sferic activity. (The
alphastable distribution as applied to lowfrequency noise is similar to a Gaussian distribution but with heavier tails.) Since many lowfrequency
communication systems operate at such locations (e.g., the polar regions), it is of value to compare the performance of receivers specifically
designed for alphastable noise with that of the receivers currently in use. Simulations are performed using real timeseries data, and
alphastable receivers are shown to give improved performance for certain signal formats.
