Generalized FMD Detection for Spectrum Sensing under Low Signal-to-Noise Ratio

Lin, Feng; Qiu, Robert C.; Hu, Zhen; Hou, Shujie; Browning, James P.; Wicks, Michael C.

doi:10.1109/lcomm.2012.030512.112164

Cited by 28 publications

(17 citation statements)

References 8 publications

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“…According to Lemma 1, the signal detection in large array is translated to a standard high dimensional signal detection problem. Classical methods include detecting the ratio of biggest and smallest eigenvalue, detecting the trace of covariance matrix [9], and LRT [8]. Among them, LRT has a decent history and has been widely used.…”

Section: Application: Function Of Detectionmentioning

confidence: 99%

Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Qiu

et al. 2019

IEEE Access

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This paper addresses the asymptotic behavior of a particular type of information-plus-noise-type matrices, where the column and row number of the matrices are large and of the same order, while signals are diverged and time delays of the channel are fixed. We prove that the empirical spectral distribution (ESD) of the large dimension sample covariance matrix and a well-studied spiked central Wishart matrix converge to the same distribution. As an application, an asymptotic power function is presented for the general likelihood ratio statistics for testing the presence of signal in large array signal processing.

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Section: Application: Function Of Detectionmentioning

confidence: 99%

Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Qiu

et al. 2019

IEEE Access

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“…For this purpose, a number of spectrum sensing methods have been proposed and investigated in [10][11][12][13][14][15][16][17]. Under no prior knowledge about the wideband signal, energy detection (ED) has been shown to be the most popular technique in cooperative sensing thanks to its low computational power requirements on wireless devices [3,13].…”

Section: Introductionmentioning

confidence: 99%

“…However, energy detection is limited by the signalto-noise ratio (SNR) wall and has high probability of false alarm [14]. In order to overcome these shortcomings, eigenvalue-based spectrum sensing methods have been proposed [14,15], which are mainly based on the asymptotic or limiting distribution of extreme eigenvalues in order to overcome the noise uncertainty problem. Unfortunately, they cannot be extended to a more general dimensional setting due to their daunting computational cost.…”

Section: Introductionmentioning

confidence: 99%

Wideband signal detection for cognitive radio applications with limited resources

Men

Chargé

Wang

et al. 2019

EURASIP J. Adv. Signal Process.

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Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many applications, spectrum sensing in cognitive radio (CR) is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. Aiming at using small sample size, a statistical model of samples is given based on Student's t distribution. However, the limited number of channel observations brings a reduction of confidence in the decision. A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer (D-S) decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method (ED) and the decision fusion method when small sample size is used.

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“…Applications of kernel-based learning in cognitive radio network have been proposed in literature [21], the algorithms after kernel mapping have gained significant performance improvements over their linear counterparts at the price of generally higher computational complexity. Generalized function of matrix detection (FMD) has been employed for spectrum sensing, through the use of the function of random matrix and matrix inequality [22]- [24]. A two-dimensional sensing framework has been proposed for spatial-temporal opportunity detection in cognitive radio [25], which exploits correlations in time and space simultaneously by fusing sensing results in a spatialtemporal sensing window.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

Lin

Qiu

Browning

2015

IEEE Trans. Veh. Technol.

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Spectrum sensing is a fundamental component of cognitive radio. How to promptly sense the presence of primary users is a key issue to a cognitive radio network. The time requirement is critical in that violating it will cause harmful interference to the primary user, leading to a system-wide failure. The motivation of our work is to provide an effective spectrum sensing method to detect primary users as soon as possible. In the language of streaming based real-time data processing, short-time means small-sized data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid's concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV signal, show that this proposed method can operate either using smaller-sized data or working under lower SNR environment. ). J. P. Browning is with Sensors Directorate, AFRL/RYMD,

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Generalized FMD Detection for Spectrum Sensing under Low Signal-to-Noise Ratio

Cited by 28 publications

References 8 publications

Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

Wideband signal detection for cognitive radio applications with limited resources

Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

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