Cyclostationary feature based multiresolution spectrum sensing approach for DVB-T and wireless microphone signals

Adoum, Bakhit Amine; Jeoti, Varun

doi:10.1109/iccce.2010.5556803

Cited by 13 publications

(8 citation statements)

References 5 publications

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“…To overcome this problem, integration of the multiscale transforms is done and the multiscale products [5] and multi-scale sums [2] are obtained.…”

Section: Edge Detection Using Dyadic Wavelet Transformsmentioning

confidence: 99%

“…In this paper we propose an adaptive spectrum sensing technique which chooses the optimum wavelet function for the given spectrum after analysis of the nature of peaks in the power spectral density of the spectrum. The traditional techniques use a particular wavelet function for the problem of edge detection as explained in [1], followed by energy detection or periodicity detection [2], [3]. But a single wavelet function cannot be effectively used for the real time spectrum data whose PSD characteristics differ vastly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Divakaran¹,

P²,

R³

2011

IJCSIT

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“…To overcome this problem, integration of the multiscale transforms is done and the multiscale products [5] and multi-scale sums [2] are obtained.…”

Section: Edge Detection Using Dyadic Wavelet Transformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Divakaran¹,

P²,

R³

2011

IJCSIT

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“…Therefore, much more attentions are paid to the cyclostationary feature detection method, for its high accuracy under low SNR and independence on priori information of primary signal. There are many researches on cyclostationary detection [5][6][7]. The cycle frequency domain profile (CDP) is used for signal detection and classification in [5].…”

Section: Introductionmentioning

confidence: 99%

Novel Compressed Sensing Algorithm Based on Modulation Classification and Symbol Rate Recognition

Zhang

et al. 2014

Wireless Pers Commun

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In the complex and changing radio environment, how to achieve the fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge. A novel compressed sensing algorithm based on modulation classification and symbol rate recognition is proposed by using the minimal sampling rate to detect spectrum holes. It is more efficient than the Nyquist sampling rate and traditional compressed sampling rate, which requires the reconstruction of the original signal. Simulation results show that it can further decrease the compressed sampling rate depending on the relation between compression ratio with modulation scheme and symbol rate. Furthermore, in order to improve the accuracy of modulation classification and symbol rate recognition, a compressed sensing and wavelet transform (CS-WT) feature detector is proposed to perform wideband detection in low SNR condition. Simulation results show that CS-WT feature detector can effectively reduce the noise introduced by CS process. Given the false alarm of 0.05 and the detection probability of 0.9, the detection probability of proposed CS-WT feature detection algorithm can be improved 4 dB compared to traditional cyclostationary detection. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm.

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“…is seen that the SLHT can sense the WM signal in heavier noise than the LHT and ED, due to the higher performance on signal concentration of STLVT when dealing with multicomponent signal. In [103], the LHT was verified to be a better method than other sensing approach such as based on the STFT-HT, pseudo WVD-HT, cyclostationary feature [111,112], singular value decomposition [119] and energy with minimum eigenvalue [120]. As the proposed SLHT performs even better, it is considered as a preferred sensing method in strong noise.…”

Section: Simulationsmentioning

confidence: 99%

“…The WM signal is one of the licensed signals in the locally TV bands, where the CR users have been permitted to communicate in these bands on a basis of IEEE 802.22 [110]. The WM signal can be described as existing sensing approaches such as the cyclostationary feature based method may not be able to detect them when SNR being smaller than −10 dB [111,112]. As their frequencies change with time, a time-frequency technique could reveal and detect them effectively [32,113].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis on applications of LV distribution.

Luo¹

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I would like to express my sincere thanks and appreciation to my supervisor Dr. Guoan Bi for his invaluable guidance, support and suggestions. His knowledge, suggestions, and discussions help me to become a capable researcher. His encouragement also helps me to overcome the difficulties encountered in my research. I also want to thank my senior graduate fellow Dr. Xiaolei Lv for his generous help, technical and personal suggestions. Without his guidance, insightful discussions and continuous encouragements, I could not have achieved today's results. I am very grateful to the members of our team in NTU. It is a pleasure to collaborate with my team mates, Dr.

show abstract

Cyclostationary feature based multiresolution spectrum sensing approach for DVB-T and wireless microphone signals

Cited by 13 publications

References 5 publications

Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Novel Compressed Sensing Algorithm Based on Modulation Classification and Symbol Rate Recognition

Performance analysis on applications of LV distribution.

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