Signal Detection Based on Cyclic Autocorrelation under Noise Uncertainty

2013 ◽  
Vol 336-338 ◽  
pp. 1733-1737
Author(s):  
Chao Wang ◽  
Li Qiang Tian
Keyword(s):  
Cognitive Radio ◽  
Signal Detection ◽  
Autocorrelation Function ◽  
Computational Cost ◽  
Noise Power ◽  
Feature Detector ◽  
Robust Detection ◽  
Low Snr ◽  
Uncertainty Model ◽  
Snr Wall

Signal detection is a key enabler of cognitive radio. This paper considers the detection signals in uncertain low SNR environments. We propose a feature detector based on cyclic autocorrelation function of signal. Compared with other feature detector based on cyclic spectral, the proposed detector need lower computational cost than computational cyclic spectrum. Similar radiometer detector,SNR wall also exists in noise power uncertainty model. Beyond this SNR wall robust detection is impossible.Detection performance including the SNR wall is proved.

Performance Analysis of a Robust Matched Subspace Detector

2011 ◽  
Vol 480-481 ◽  
pp. 775-780
Author(s):  
Ting Jun Li
Keyword(s):  
Performance Analysis ◽  
Matched Filter ◽  
Alternative Hypothesis ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Noise Power ◽  
Test Statistic ◽  
Robust Detection ◽  
Low Snr ◽  
Useful Signal

The area of robust detection in the presence of partly unknown useful signal or interference is a widespread task in many signal processing applications. In this paper, we consider the robustness of a matched subspace detector in additive white Gaussian noise, under the condition that the noise power is known under null hypothesis, and unknown under alternative hypothesis when the useful signal triggers an variation of noise power, and we also consider the mismatch between the signal subspace and receiver matched filter. The test statistic of this detection problem is derived based on generalized likelihood ratio test, and the distribution of the test statistic is analysis. The computer simulation is used to validate the performance analysis and the robustness of this algorithm at low SNR, compared with other matched subspace detectors.

scholarly journals A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

2021 ◽  
Author(s):  
Garima Mahendru
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Communication Link ◽  
Noise Power ◽  
Radio System ◽  
Low Snr ◽  
Detection Probabilities ◽  
Analytical Result ◽  
Novel Concept

Abstract Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.

scholarly journals Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Hassaan Bin Ahmad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Detection Threshold ◽  
Computational Cost ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Adaboost Algorithm ◽  
Low Snr

Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user’s (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU’s signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

A Method for Efficient Implementation of a Radial-Based Noise Power Estimator for Weather Radars

2021 ◽  
Author(s):  
Martin Hurtado
Keyword(s):  
Closed Form ◽  
Background Noise ◽  
Computational Cost ◽  
Weather Radar ◽  
Efficient Implementation ◽  
Noise Power ◽  
Free Range ◽  
Weather Radars ◽  
Low Computational Cost

AbstractIn a previous work, a weather radar algorithm with low computational cost has been developed to estimate the background noise power from the data collected at each radial. The algorithm consists of a sequence of steps designed to identify signal-free range volumes which are subsequently used to estimate the noise power. In this paper, we derive compact-closed form expressions to replace the numerical formulations used in the first two steps of the algorithm proposed in the original paper. The goal is to facilitate efficient implementation of the algorithm.

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