Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

Zheng Dou,Wenwen Li,Chengzhuo Shi,Yun Lin

doi:10.1186/s13638-017-0983-3

Zheng Dou, Wenwen Li + Show 2 more

Open Access

https://doi.org/10.1186/s13638-017-0983-3

Copy DOI

Abstract

Motivated by the practical and accurate demand of intelligent cognitive radio (CR) sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the non-Gaussian colored noise based on α stable process and the sensing method is improved fractional low-order moment (FLOM) detection algorithm with balance parameter. First, we establish the non-Gaussian colored noise model through combining α-distribution with a linear system represented by a matrix. And a fitting curve of practical noise data is given to verify the validity of the proposed model. Then we present a parameter estimation method with low complexity to obtain the balance parameter, which is an important part of the detection algorithm. The balance parameter-based FLOM (BP-FLOM) detector does not require any a priori knowledge about the primary user signal and channels. Monte Carlo simulations clearly demonstrate the performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α, and the number of detectors in sensing networks.

Highlights

1 Introduction The cognitive radio (CR) nodes with sensing and adaptive abilities have been recognized as a promising solution [1] to realize the next-generation intelligent sensing networks; the key ideas behind detector nodes lie in sensing spectrum information accurately under the practical noise background
3 BP-fractional low-order moment (FLOM)-based spectrum sensing we propose a new spectrum sensing scheme, namely balance parameter-based fractional loworder moment (BP-FLOM) detector, for the non-Gaussian colored noise background
3.2 Spectrum sensing based on balance parameter We have known that the fractional low-order moment detection (FLOM) has a good performance under the symmetric α-stable (SαS) distribution noise that is presented in [4], and it is more suitable than Cauchy detector

Summary

Introduction

The cognitive radio (CR) nodes with sensing and adaptive abilities have been recognized as a promising solution [1] to realize the next-generation intelligent sensing networks; the key ideas behind detector nodes lie in sensing spectrum information accurately under the practical noise background. We propose a novel model to describe the non-Gaussian colored noise and present a new detection method to sensing signals. We first fit the curve of practice noise data to study its characteristics and give a novel model to present the colored non-Gaussian noise through combining symmetric α-distribution with a linear system represented by a matrix. 3.2 Spectrum sensing based on balance parameter We have known that the fractional low-order moment detection (FLOM) has a good performance under the SαS distribution noise that is presented in [4], and it is more suitable than Cauchy detector. We give the hypothesis A: The practical non-Gaussian colored noise is obtained by the linear transformation of the non-Gaussian white noise sequence obeying the SαS distribution.

Balance parameter-based FLOM detector

Conclusions