Dynamic Threshold Correction based on the Exact Statistics of Energy Detection in Spectrum Sensing

2019 ◽  
Author(s):  
Sutapa Sarkar ◽  
R. Muralishankar ◽  
Sanjeev Gurugopinath
Keyword(s):  
Spectrum Sensing ◽  
Energy Detection ◽  
Dynamic Threshold ◽  
Threshold Correction

scholarly journals Energy Optimized Cooperative Spectrum Sensing in Cognitive Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3316-3319
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Threshold Energy ◽  
Energy Detection ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Dynamic Threshold ◽  
Cognitive Network ◽  
Spectrum Scarcity ◽  
Detection Schemes

The spectrum scarcity problem is addressed by number solutions by various researchers in cognitive network field. The dynamic spectrum allocation using cooperative spectrum sensing is required to analyze with respect to errors present in detection due to fixed threshold. The spectrum allocation on the basis of demand may involve the priority based requests for spectrum allocation. The contribution of this paper is for evaluating the performance of dynamic threshold energy detection schemes which are error dependent and hence minimization of errors. The performance evaluation of two methods with error minimization strategy are evaluated and results are compared to know the performance oriented dependency parameters in dynamic threshold methods and to provide the platform strategy for energy optimization in cooperative sensing.

A Dynamic Threshold Optimization Algorithm for Spectrum Sensing with Energy Detection in Cognitive Radio

2012 ◽  
Vol 236-237 ◽  
pp. 917-922
Author(s):  
Wei Ran Wang ◽  
Shu Bin Wang ◽  
Xin Yan Zhao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Dynamic Threshold ◽  
Threshold Optimization ◽  
Sensing Performance

In order to improve an efficiency of energy detection for a spectrum sensing in cognitive radio (CR), this paper proposes a dynamic threshold optimization algorithm. The traditional energy detection algorithm uses a fixed threshold, and can't guarantee always the optimal sensing performance in any environment. The improvement for sensing performance need to minimize the undetected probability and the probability of false alarm, and it is dissimilar for different CR users to accept these two errors. We improve the traditional energy detection algorithm, and firstly introduce a preference factor to characterize CR users’ different requirements for these two errors, then, propose a dynamic threshold optimization algorithm by minimizing integrated detection error for different signal-to-noise ratio (SNR). The simulation results show that the proposed algorithm effectively reduces the integrated spectrum sensing error, and increases the probability of detection, especially in low SNR.

Research on Dynamic Threshold Based Energy Detection in Cognitive Radio Systems

2012 ◽  
Vol 462 ◽  
pp. 506-511 ◽  
Author(s):  
Gui Cai Yu ◽  
Cheng Zhi Long ◽  
Man Tian Xiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Noise Power ◽  
Noise Uncertainty ◽  
Spectrum Detection ◽  
Radio Systems ◽  
Short Time

In cognitive radio networks, nodes should have the capability to decide whether a signal from a primary transmitter is locally present or not in a certain spectrum within a short detection period. Traditional spectrum sensing schemes based on fixed threshold are sensitive to noise uncertainty, a fractional fluctuate of average noise power in a short time can lead the performance of spectrum detection drop seriously. This paper presents a new spectrum detection algorithm based on dynamic threshold. Theoretical results show that the proposed scheme debate the noise uncertainty, and good detection performance can be gained, if suitable dynamic threshold is chosen. In other words, the proposed scheme can enhance the robustness against noise and improve the capacity of spectrum sensing.

scholarly journals Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

2019 ◽  
Vol 9 (2) ◽  
pp. 2655-2661
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Kernel Function ◽  
Feature Space ◽  
Energy Detection ◽  
Spectrum Efficiency ◽  
Dynamic Threshold ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Space Data

Cognitive radio (CR) is a new technology that is proposed to improve spectrum efficiency by allowing unlicensed secondary users to access the licensed frequency bands without interfering with the licensed primary users. As there are several methods available for spectrum sensing, the energy detection (ED) is more popular due to its simple implementation. However, ED is more vulnerable to the noise uncertainty so for that reason, we present a robust detector using signal to noise ratio (SNR) with dynamic threshold energy detection technique is combined with the kernel principal component analysis (KPCA) in Cognitive Radio Networks (CRN). The primary purpose of kernel function is to ensure that its dependency relies on inner-product of data without the feature space data requirement. In this paper, with the aid of kernel function the spectrum sensing with the leading eigenvector approach is modified to a feature space of higher dimensionality.By introducing of efficient detection system with dynamic threshold facility helps the better detection levels even low SNR values with quite a lot of noise uncertainty levels. The simulation results of the proposed system reveal that KPCA outperforms with that of traditional PCA in terms of false alarm rate, detector performance when tested under various uncertainties for orthogonal frequency division multiplexing signal.

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