Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems

2014 ◽  
Author(s):  
Kriti Chabbra ◽  
Garima Mahendru ◽  
P. Banerjee
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Dynamic Threshold ◽  
Noise Uncertainty ◽  
Cognitive Radio Systems ◽  
Radio Systems

scholarly journals Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 631
Author(s):  
Josip Lorincz ◽  
Ivana Ramljak ◽  
Dinko Begušić
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Method ◽  
Detection Threshold ◽  
Energy Detection ◽  
Primary User ◽  
Noise Uncertainty ◽  
Cognitive Radio Systems ◽  
Radio Systems ◽  
The Impact

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

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.

Energy Detection Approach for Spectrum Sensing in Cognitive Radio Systems with the Use of Random Sampling

2014 ◽  
Vol 79 (2) ◽  
pp. 1053-1061 ◽  
Author(s):  
Hayat Semlali ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
Abdelilah Ghammaz ◽  
Jean-François Diouris
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Random Sampling ◽  
Energy Detection ◽  
Detection Approach ◽  
Cognitive Radio Systems ◽  
Radio Systems

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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