Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers

Abstract

This paper addresses non-Gaussian statistical modeling of interference as a superposition of a large number of small effects from terminals/scatterers distributed in the plane/volume according to a Poisson point process. This problem is relevant to multiple access communication systems without power control and radar. Assuming that the signal strength is attenuated over distance r r r as 1=r m 1=r m 1=r m , we show that the interference/clutter could be modeled as a spherically symmetric-stable noise. A novel approach to stable noise modeling is introduced based on the LePage series representation. This establishes grounds to investigate practical constraints in the system model adopted, such as the finite number of interferers and nonhomogeneous Poisson fields of interferers. In addition, the formulas derived allow us to predict noise statistics in environments with lognormal shadowing and Rayleigh fading. The results obtained are useful for the prediction of noise statistics in a wide range of environments with deterministic and stochastic power propagation laws. Computer simulations are provided to demonstrate the efficiency of the stable noise model in multiuser communication systems. The analysis presented will be important in the performance evaluation of complex communication systems and in the design of efficient interference suppression techniques.

A normalized Fractionally lower-order moment algorithm for Space-Time Adaptive Processing

“…1. This type of clutters have been reported in many scenarios [5], [6], [8]. It is also found [4] that high resolution sea clutters also have heavier tails than the compound K distribution.…”

“…Several statistical models have been used to describe the impulsive non-Gaussian clutter environment including the compound K [3], [4] and complex alpha-stable [6]- [8]. The compound complex Gaussian model is a popular approach, where the clutter/noise process is the product of two random processes: X = √ τ · G, with τ being the texture and G the speckle .…”

Applications and Comparison of Continuous Wavelet Transforms on Analysis of A-wave Impulse Noise

“…The results of this study provide useful information for applying wavelet transform on analysis of A-wave impulse noise signals, and accordingly improve understanding of A-wave impulse noise induced hearing loss. However, other types of impulse noise, such as impact noise (Henderson, Hamernik, 1986) and alpha-stable impulsive noise (Ilow, Hatzinakos, 1998) are not included in this wavelet analysis framework. Further study will be done to apply CWT for analysis and characterization of different types of noises, including continuous noise, impulse noise, and complex noise.…”

Wireless Communication in Dynamic Interference

“…In particular, it was shown that the interfering signal in each time slot is α-stable. The α-stable interference model is also known to be a good approximation of the true interference distribution when the radius of the network is large, there are no guard zones and the active interferer set changes rapidly [6][7][8].…”