Robust, Non-Gaussian Wideband Spectrum Sensing in Cognitive Radios

In this paper, we propose detectors (both parametric and robust) for wideband spectrum sensing in cognitive radios (CR's). The proposed detectors are able to detect spectral activity over a wide frequency range, while assuming little knowledge about the signals of interest. The parametric detec...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications 2014-11, Vol.13 (11), p.6410-6421
Main Authors: Bkassiny, Mario, Jayaweera, Sudharman K.
Format: Article
Language:English
Subjects:
Cognitive radio
Detection
Detectors
Estimation
Gaussian noise
Noise
Non-Gaussian
Normal distribution
Regression
Robustness
Sensors
Spectra
Wideband
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose detectors (both parametric and robust) for wideband spectrum sensing in cognitive radios (CR's). The proposed detectors are able to detect spectral activity over a wide frequency range, while assuming little knowledge about the signals of interest. The parametric detector is based on a locally optimal (LO) Neyman-Pearson (NP) test and assumes a known non-Gaussian noise distribution. The corresponding decision statistic of the LO NP detector is expressed in frequency domain, allowing to identify the active channels within the wide frequency band of interest. On the other hand, for situations in which the noise distribution is only approximately known, we propose a robust signal detector that is immune to deviations of the noise model from a certain nominal distribution. The proposed wideband robust detector is based on a robust spectral estimator and is formulated as a non-linear regression. This regression problem can be solved using a fixed-point iteration algorithm at a quadratic computational complexity, in contrast with the Newton's method which would have cubic complexity order. The simulation results show that the proposed detectors can achieve better detection performance in the presence of non-Gaussian noise, compared to existing detectors under the same conditions.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2014.2346772