A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

Zeng, Yonghong; Liang, Ying-Chang; Hoang, Anh Tuan; Zhang, Rui

doi:10.1155/2010/381465

Cited by 585 publications

(401 citation statements)

References 80 publications

Supporting

Mentioning

388

Contrasting

Unclassified

Order By: Relevance

“…In particular, sensing techniques based on the eigenvalues of the received sample covariance matrix (see, for instance, [4]- [10]) recently emerged as a promising solution, as they do not require a priori assumptions on the signal to be detected, and typically outperform the popular energy detection method when multiple sensors are available. Eigenvaluebased detection (EBD) schemes can be further divided into two categories: methods that assume knowledge of noise level (referred to as "semi-blind" [4]), and methods that do not assume this knowledge ("blind"). Methods belonging to the first class provide better performance when the noise variance is known exactly, whereas blind methods are more robust to uncertain or varying noise level.…”

Section: Introductionmentioning

confidence: 99%

Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

Nadler

Penna

Garello

2011

2011 IEEE International Conference on Communications (ICC)

129

109

View full text Add to dashboard Cite

Abstract-In this paper we consider signal detection in cognitive radio networks, under a non-parametric, multi-sensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvalue-based methods, namely Roy's largest root test, which requires knowledge of the noise variance, and the generalized likelihood ratio test, which can be interpreted as a test of the largest eigenvalue vs. a maximum-likelihood estimate of the noise variance. The detection performance of the two considered methods is expressed by closed-form analytical formulas, shown to be accurate even for small number of sensors and samples. We then derive an expression of the gap between the two detectors in terms of the signal-to-noise ratio of the signal to be detected, and we identify critical settings where this gap is significant (e.g., small number of sensors and signal strength). Our results thus provide a measure of the impact of noise level knowledge and highlight the importance of accurate noise estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

Nadler

Penna

Garello

2011

2011 IEEE International Conference on Communications (ICC)

129

109

View full text Add to dashboard Cite

show abstract

“…Among them, energy detection has been widely used since it does not require any a priori knowledge of the primary signals and has much lower complexity than the other two schemes. In addition, it does not need any prior information about the PUs' signals [5].…”

Section: IImentioning

confidence: 99%

Performance Analysis of OFDM based Cognitive Radio Network through Energy Detection

2017

IJIET

View full text Add to dashboard Cite

With the development of a new and ever expanding wireless applications and services, spectrum resources are facing in demands. In present scenario, the spectrum allotment has been done by providing each new service with its own fixed frequency Slot. Most of the user's spectrum is already assigned, so it becomes very difficult to find spectrum for other users or existing users. This leads to the scarcity of available spectrum and inefficient channel utilization. Cognitive radio is a novel technology which improves the spectrum utilization by allowing secondary user to borrow unused radio spectrum from primary licensed users or to share the spectrum with the primary users. Present paper deals with the spectrum sensing in which multiple users detect the spectrum gap through energy detection and investigate the detection performance in an efficient and implementable way. Simulation results showed that the probability of detection is achieved at small value SNR in case of OFDM modulation as compare the other and simple cognitive environment.

show abstract

“…Different methods such as matched filter [7,8], energy detection [9,10], cyclostationary detection [11][12][13][14], eigenvalue detection [15][16][17], and covariance-based detection [18] are proposed for spectrum sensing in the literature. These methods have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Çiflikli

Ilgin

2018

Teh. vjesn.

View full text Add to dashboard Cite

Abstract:The eigenvalue based detection is a low-cost spectrum sensing method that detects the presence of primary user signal at a desired frequency. In this study, the largest eigenvalue distribution used in eigenvalue based detection methods is expressed using a new centering and scaling coefficients adjustment. Thus, the detection probability (Pd) and false detection probability (Pfa) equations for the maximum-minimum eigenvalue (MME), maximum eigenvalue to trace (MET) and maximum eigenvalue-geometric mean (ME-GM) have been obtained again. Weibull fading channels are the best model for wireless communication. For this reason, the studies were simulated in Weibull fading channels and analysed in detail with receiver operating characteristic curves (ROC). The results were compared with traditional methods and found to be more accurate.

show abstract

A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

Cited by 585 publications

References 80 publications

Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

Performance Analysis of OFDM based Cognitive Radio Network through Energy Detection

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Contact Info

Product

Resources

About