Discrete Wavelet Transform Based Spectrum Sensing in Futuristic Cognitive Radios

Chandran, Anand; Karthik, R.; Кумар, Арун; Siva, M. Subramania; Iyer, Uma S.; Ramanathan, R.; Naidu, Roshini C.

doi:10.1109/icdecom.2011.5738461

Cited by 15 publications

(6 citation statements)

References 4 publications

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“…It provides a Multi Resolution Analysis (MRA) feature letting analyze not only stationary but also non-stationary signals successfully [13]. Additionally, this technique is preferred because it requires low computational complexity comparing to especially Fourier based techniques [10]. MRA feature is achieved by using scaled and shifted in time wavelet functions.…”

Section: Spectrum Sensing Methods Using Wavelet Transformmentioning

confidence: 99%

“…In this regard, Wavelet Transform (WT) is quite advantageous approach compared to the others. For example, while fast Fourier transform based techniques have computational complexity of order N(log 2 N), wavelet transform based techniques have computational complexity of order N [10], where N is data length showing observation period. Therefore, WT based techniques for spectrum sensing has widely been applied in literature [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Energy based spectrum sensing using wavelet transform for fading channels

Bektas

Akan

Odabasioglu

2012

2012 IV International Congress on Ultra Modern Telecommunications and Control Systems

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In this paper, we present a spectrum sensing algorithm based on energy level at different frequency bands using wavelet transform for Cognitive Radio (CR) applications. Existence of a primary user's signal may be detected by searching for wavelet coefficients having energy higher than a threshold value corresponding to communication channel noise energy. The wavelet based algorithm is tested with Binary Phase Shift Keying (BPSK) modulated signals under two channel models introducing fading channel affect and additive white Gaussian noise with different Signal to Noise Ratios (SNRs). In order to evaluate accuracy of the algorithm, probability of detection is evaluated.

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Section: Spectrum Sensing Methods Using Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy based spectrum sensing using wavelet transform for fading channels

Bektas

Akan

Odabasioglu

2012

2012 IV International Congress on Ultra Modern Telecommunications and Control Systems

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“…It provides a Multi Resolution Analysis (MRA) feature by analyzing not only stationary but also non-stationary signals [6]. Ad ditionally, this technique is preferred because it requires low computational complexity comparing with especially Fourier based techniques [7].…”

Section: A Wavelet Transfann (Wt)mentioning

confidence: 99%

“…J -level wavelet packet transform decomposition structure diagram is given in Fig.2. packet transform decomposes the noisy signal x( n) into 2 j subbands, the corresponding wavelet coefficient sets as [9] d{ m = WP{x (n),j },n = 1,2" " ,N (7) Where d{ m denotes the mth coefficient of the ith subband for the jth level, and m = 1"" ,N/2 j ,k = 1 , 2"" ,2 j. …”

Section: A Wavelet Transfann (Wt)mentioning

confidence: 99%

Performance evaluation of spectrum sensing based on wavelet entropy for cognitive radio networks

Chu

Niu

et al. 2014

2014 International Symposium on Wireless Personal Multimedia Communications (WPMC)

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As wireless communication services grow quickly, the seriousness of spectrum scarcity has been on the rise gradually. Cognitive radio (CR) is viewed as a novel approach for improving the utilization of the radio electromagnetic spectrum.Among its fundamental functions, the most important function is spectrum sensing. This paper develops spectrum sensing based on wavelet entropy(WE) in cognitive radio networks. Then we compared with Spectrum Sensing based on wavelet packet entropy(WPE). According to the wavelet transform theory, we proposed spectrum sensing approach which has two advantages: low computational complexity and less response time. When the detected signal is concentrated in the low frequency band, our detection method is cost-effective. Simulation results show that the presented spectrum sensing algorithm is robust against noise uncertainty. Furthermore, the simulation results also indicate that the detection probability of the two spectrum sensing approaches is almost same to be close to 1, when the SNR is greater than -6dB.

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“…In some earlier studies, signal energy measurement based methods are emphasized because of their low computational load and sensing time [10]- [11]. However in later studies, cyclostationarity based spectrum sensing techniques that are more stable to unknown or variable noise levels or against uncertainty are presented [12]- [13].…”

Section: Introductionmentioning

confidence: 99%

Optimum relay selection for cooperative spectrum sensing and transmission in cognitive networks

Kartlak

Odabasioglu

Akan

2014

2014 22nd Signal Processing and Communications Applications Conference (SIU)

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In this paper, cyclostationarity based cooperative spectrum sensing is presented to detect the idle bands and then locate the secondary users into these bands. The aim is to reduce the processing complexity with using a relay for transmission and spectrum sensing. As such, an optimum relay is selected to perform both cooperative communication and cyclostationarity based spectrum sensing. Performance of transmission, probability of detection, and probability of missing are presented via computer simulations. Results show that proposed jointly optimized relay selection scheme provides sufficient performance for both transmission and spectrum sensing.

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Discrete Wavelet Transform Based Spectrum Sensing in Futuristic Cognitive Radios

Cited by 15 publications

References 4 publications

Energy based spectrum sensing using wavelet transform for fading channels

Energy based spectrum sensing using wavelet transform for fading channels

Performance evaluation of spectrum sensing based on wavelet entropy for cognitive radio networks

Optimum relay selection for cooperative spectrum sensing and transmission in cognitive networks

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