MMME and DME: Two new eigenvalue-based detectors for spectrum sensing in cognitive radio

Bollig, Andreas; Mathar, Rudolf

doi:10.1109/globalsip.2013.6737125

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different eigenvalue based detection methods are MME [10], energy with minimum eigenvalue (EME) [10], maximum minus minimum eigenvalue (MMME) [11], difference of means of eigenvalue (DME) [11], LE i.e. MED [12], minimum eigenvalue (ME) [13], two stage spectrum sensing detection (ED and CMME) [14], energy and eigenvalue based detections combined as fully blind and self adaptive [15].…”

Section: Network Protocols and Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio

Patil¹,

Lande²,

Naikwadi³

2016

NPA

View full text Add to dashboard Cite

Spectrum scarcity has been encountered as a leading problem when launching new wireless services. To overcome this problem, cognitive radio is an optimistic solution. Spectrum sensing is a prominent task of cognitive radio. Over the past decade, numerous spectrum sensing algorithms have been proposed. In this paper, we present a comprehensive survey of evolutionary achievements of eigenvalue based spectrum sensing algorithms. The correlation between signal samples due to oversampling, multipath or multiple receivers gets reflected on the eigenvalues of the covariance matrix. It has been observed that different combinations of eigenvalues are used as test statistics and the distribution of eigenvalues and derivation of probability of detection is based on RMT (Random Matrix Theory). The main advantage offered by these algorithms is their robustness to noise uncertainty which severely affect www.macrothink.org/npa 58 Network Protocols and Algorithms ISSN 1943-3581 2016 other methods. Furthermore, they do not require accurate synchronization. These detections can be used for different signal detection applications without any prior information of signal or noise. To evaluate the performance of eigenvalue based spectrum sensing techniques under fading channels, we have simulated maximum to minimum eigenvalue based Detection (MME) and maximum eigenvalue based detection (MED) for Rician fading channel. Simulation results shows that MME is much better than MED.

show abstract

Section: Network Protocols and Algorithmsmentioning

confidence: 99%

“…To tackle this issue, MME can be modified as MMME. In MMME detection, maximum minus minimum eigenvalue is compared with the threshold for test static [11].…”

Section: Maximum Minus Minimum Eigenvalue Detectionmentioning

confidence: 99%

A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio

Patil¹,

Lande²,

Naikwadi³

2016

NPA

View full text Add to dashboard Cite

show abstract

“…Detecting spectrum holes is known as spectrum sensing in the literature and there have been a number of publications on detection schemes based on various signal features, see [2] for a fairly recent review. Among those features, the sample covariance matrix and particularly its eigenvalues have been used in order to design several detectors, see e.g., [3]- [8]. A well known one, which will be relevant in this work, is the ratio of the maximum to minimum eigenvalue (MME), i.e.…”

Section: Introductionmentioning

confidence: 99%

Quickest Eigenvalue-Based Spectrum Sensing using Random Matrix Theory

Arts,

Bollig,

Mathar

2015

Preprint

Self Cite

View full text Add to dashboard Cite

We investigate the potential of quickest detection based on the eigenvalues of the sample covariance matrix for spectrum sensing applications. A simple phase shift keying (PSK) model with additive white Gaussian noise (AWGN), with 1 primary user (PU) and K secondary users (SUs) is considered. Under both detection hypotheses H0 (noise only) and H1 (signal + noise) the eigenvalues of the sample covariance matrix follow Wishart distributions. For the case of K = 2 SUs, we derive an analytical formulation of the probability density function (PDF) of the maximum-minimum eigenvalue (MME) detector under H1. Utilizing results from the literature under H0, we investigate two detection schemes. First, we calculate the receiver operator characteristic (ROC) for MME block detector based on analytical results. Second, we introduce two eigenvaluebased quickest detection algorithms: a cumulative sum (CUSUM) algorithm, when the signal-to-noise ratio (SNR) of the PU signal is known and an algorithm using the generalized likelihood ratio, in case the SNR is unknown. Bounds on the mean time to falsealarm τ fa and the mean time to detection τd are given for the CUSUM algorithm. Numerical simulations illustrate the potential advantages of the quickest detection approach over the block detection scheme.

show abstract

A review of the noise uncertainty impact on energy detection with different OFDM system designs

Lörincz

Ramljak²,

Begušić

2019

Computer Communications

View full text Add to dashboard Cite

MMME and DME: Two new eigenvalue-based detectors for spectrum sensing in cognitive radio

Cited by 6 publications

References 9 publications

A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio

A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio

Quickest Eigenvalue-Based Spectrum Sensing using Random Matrix Theory

A review of the noise uncertainty impact on energy detection with different OFDM system designs

Contact Info

Product

Resources

About