SNR wall for generalized energy detection under noise uncertainty in cognitive radio

2013 ◽  
Author(s):  
Sanket S. Kalamkar ◽  
Adrish Banerjee ◽  
Abhishek K. Gupta
Keyword(s):  
Cognitive Radio ◽  
Energy Detection ◽  
Noise Uncertainty ◽  
Snr Wall

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.

scholarly journals Noise uncertainty effect on multi-channel cognitive radio networks

2020 ◽  
Vol 10 (5) ◽  
pp. 4818
Author(s):  
Amira Osama ◽  
Heba A. Tag El-Dien ◽  
Ahmad A. Aziz El-Banna ◽  
Adly S. Tag El-Dien
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Feature Detection ◽  
Matched Filter ◽  
Energy Detection ◽  
Radio Networks ◽  
Main Process ◽  
Fluctuation Effect ◽  
Noise Uncertainty ◽  
Channel Throughput

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

SNR wall and cooperative spectrum sensing in cognitive radio under noise uncertainty

2010 ◽  
Vol 27 (5) ◽  
pp. 611-617 ◽  
Author(s):  
Haijun Wang ◽  
Xin Su ◽  
Yi Xu ◽  
Xiang Chen ◽  
Jing Wang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Noise Uncertainty ◽  
Snr Wall

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.

A Metaheuristic based approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks

2019 ◽  
Vol 13 ◽  
Author(s):  
Garima Mahendru ◽  
Anil K Shukla ◽  
L M Patnaik
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Energy Detection ◽  
Swarm Optimization ◽  
Noise Uncertainty ◽  
Low Snr

Background: : The mounting growth of wireless technology is attracting high demand for frequency spectrum. The measurements of spectrum usage depicts that a significant portion of spectrum lays unoccupied or overcrowded. The main cause of the glitch is the existing inefficient and fixed scheme of spectral allocation. Cognitive radio is one such technology that permits wireless devices to detect the unused frequency band and reconfigure its operating parameters to attain required quality of service. Objective: To permit dynamic allocation of the frequency band, spectrum sensing is performed which is an essential function of Cognitive radio and involves detection of an unused spectrum space to setup a communication link. Method: : This paper presents a meta-heuristic approach for selection of a decision threshold for energy detection based spectrum sensing. At low SNR and in presence of noise uncertainty performance of energy detection method fails. A novel adaptive double threshold based spectrum-sensing method is proposed to avoid such a sensing failure. Further, the metaheuristic approach employs Particle Swarm Optimization (PSO) algorithm to compute an optimal value of the threshold to attain robustness against noise uncertainty at low SNR. Results: : The simulation results of the proposed metaheuristic double threshold based spectrum sensing method demonstrate enhanced performance in comparison to the existing methods in terms of reduced error rate and increased detection probability. Some of the existing methods have been analyzed and compared from a survey of recent patents on spectrum sensing methods to support the new findings The concept of adaptive thresholding improves the detection probability by 39 % and 27 % at noise uncertainty of 1.02 and 1.04 respectively at a signal to noise ratio of -10 dB. Furthermore, the error probability reduces to 58% at the optimal threshold using Particle Swarm Optimization (PSO) algorithm for signal to noise ratio of -9 dB. Conclusions: : The main outcome of this work is reduction in probability of sensing failure and improvement in the detection probability using adaptive double thresholds at low SNR. Further, particle swarm optimization helps in obtaining minimum probability of error under noise uncertainty with an optimal threshold.

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