Experimental study of a Wavelet-based spectrum sensing technique

Almeida, Erika P. L.; Carvalho, Paulo S.; Cordeiro, Pedro Antero Braga; Vieira, Robson D.

doi:10.1109/acssc.2008.5074682

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…The boundaries are very sharp and the peaks are sparse and large. The Haar wavelet system is especially suited for such spectra which are characterized by well defined edges [15]. In Fig.2 the coefficients at dyadic scales are shown.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Individual frequency points are checked for the condition in (15). Accordingly, a composite data set consisting of peaks derived from both the Multi-scale sums is obtained.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Taking multiscale product of the wavelet transform or its derivative at different scales enhances the multiscale peaks due to edges while suppressing the noise. To obtain better results for edge detection, using small values of scale's' is advisable [15]. This happens only when the spectral characteristics of the channel changes abruptly.…”

Section: Multi-scale Productmentioning

confidence: 99%

“…The multi-scale product suggested in [15], and the multi-scale sum explained in [5] is plotted in Figure 3 below. It can be observed that multi-scale sum gives more accurate results than multi-scale product.…”

mentioning

confidence: 99%

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Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Divakaran¹,

P²,

R³

2011

IJCSIT

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Individual frequency points are checked for the condition in (15). Accordingly, a composite data set consisting of peaks derived from both the Multi-scale sums is obtained.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Multi-scale Productmentioning

confidence: 99%

mentioning

confidence: 99%

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Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Divakaran¹,

P²,

R³

2011

IJCSIT

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“…These sensing methods can be classified into three categories: (A) methods requiring both source signal and noise power information, (B) methods requiring only noise power information (semi-blind detection), and (C) methods requiring no information on source signal or noise power (totally blind detection). For example, likelihood ratio test [3], Matched Filter [4], and cyclo-stationary detection [5] belong to category (A); energy detection [6] and wavelet-based sensing methods [7] belong to category (B); eigenvalue-based sensing [8], covariance-based sensing [9], and blindly combined energy detection [6] belong to category (C).…”

Section: Introductionmentioning

confidence: 99%

Adaptive spectrum sensing of wireless microphones with noise uncertainty

Hassan

Nasr

2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

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Many spectrum sensing techniques have been proposed in the literature to enable cognitive radio technology. However, their reliability when primary users have very low signal-to-noise ratio (SNR) in the presence of noise uncertainty remains a challenging problem. This paper focuses on detecting wireless microphone signals in the presence of noise uncertainty. Power Spectrum Density (PSD)-based sensing has been proposed in the literature as the best sensing algorithm for wireless microphones. However, when there is noise uncertainty, PSDbased sensing performance is severely degraded. To solve this problem, eignevalues-based blind sensing, which does not need noise information, have been proposed. In this paper, we present a new adaptive spectrum sensing algorithm that outperforms both PSD-based sensing and the eigenvalues-based sensing in the presence of noise uncertainty. The algorithm combines the decisions of the two algorithms, and then, adapts the decision threshold required for the PSD-based sensing in an iterative way. Simulation results show that the proposed spectrum sensing algorithm outperforms the PSD-based sensing in the presence of 1 dB noise uncertainty by more than 2 dBs. At the same level of noise uncertainty, our algorithm outperforms the eigenvaluebased sensing by 1.2 dBs.

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