Techniques for dealing with uncertainty in cognitive radio networks

Salahdine, Fatima; Kaabouch, Naima; Ghazi, Hassan El

doi:10.1109/ccwc.2017.7868352

Cited by 29 publications

(27 citation statements)

References 40 publications

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“…Sensing a wideband spectrum using traditional approaches requires a great deal of time and resources. Fortunately, a few channels are occupied in the radio spectrum, which enables the application of compressive sensing in this area . The design of a measurement matrix that selects the occupied channels to recover the wideband signal with a small recovery error is challenging since the number of measurement is dependent on the sparsity level of the signal, which is not always available.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Fortunately, a few channels are occupied in the radio spectrum, which enables the application of compressive sensing in this area. 6,[56][57][58][59][60][61][62][63][64][65][66] The design of a measurement matrix that selects the occupied channels to recover the wideband signal with a small recovery error is challenging since the number of measurement is dependent on the sparsity level of the signal, which is not always available. To avoid an estimation of this parameter, the design of the measurement matrix has to be independent of the wideband signal sparsity.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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A performance comparison of measurement matrices in compressive sensing

Arjoune

Kaabouch

Ghazi

et al. 2018

Int J Communication

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Summary Compressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery process. Hence, the design of accurate measurement matrices is of vital importance in compressive sensing. Over the last decade, a number of measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances are strongly needed. This paper explains the foundation of compressive sensing and highlights the process of measurement by reviewing the existing measurement matrices. It provides a 3‐level classification and compares the performance of 8 measurement matrices belonging to 4 different types using 5 evaluation metrics: the recovery error, processing time, recovery time, covariance, and phase transition diagram. The theoretical performance comparison is validated with experimental results, and the results show that the Circulant, Toeplitz, and Hadamard matrices outperform the other measurement matrices.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

A performance comparison of measurement matrices in compressive sensing

Arjoune

Kaabouch

Ghazi

et al. 2018

Int J Communication

Self Cite

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“…Also all these techniques failed to perform efficiently for a wider spectral range and so could not be applied to the practical applications of cognitive radio requiring wideband spectrum sensing. The range of spectrum sensing varies from some hundreds of megahertz to gigahertz for the desired performance in terms of throughput and efficiency as the maximum bit rate is directly proportional to the spectral bandwidth as per the Shannon's formula [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Compressive Sampling Based Wideband Spectrum Sensing in Cognitive Radio Network using Wavelet Transform

Nigam*,

Pawar,

Sharma

2019

IJRTE

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This paper deals with the implementation of sub Nyquist sampling for the efficient wideband spectrum sensing in cognitive radio network. Cognitive radio is a very promising technology in the field of wireless communication which has drastically changed the spectral dynamics through the opportunistic utilization of frequency band by the secondary users when it is not utilized by the primary users. The complexity of spectral detection strategy is reduced using the compressive sensing method. Bayesian technique is utilized in the compressive sampling to deal with uncertainty of the process and increase the speed of detection. This technique recovers the wideband signals even with few measurements via Laplace prior and Toeplitz matrix. Sparse signal recovery algorithm is used for the extraction of primary user frequency location. The condition of the detection of primary user even in the low regulated transmission from unlicensed user is been resolved in this paper through Wavelet transform. This approach enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation analysis is performed to verify the effectiveness of the proposed technique over the cognitive radio network.

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“…This sampling technique can only be used for sparse signal with a finite set of pure sinusoids. 16,17 Moreover, the implementation of such techniques using random measurements is costly. The random taps, representing the sensing matrix coefficients, are generated as a normal Bernoulli distribution with zero mean and unit variance.…”

Section: Introductionmentioning

confidence: 99%

“…However, these matrices are unstructured, dense, require high memory space, correspond to slow multiplication, and cannot deal with uncertainty. 16,17 Moreover, the implementation of such techniques using random measurements is costly. 18 For the third process, recovery, a number of recovery techniques have been proposed under the spectrum sensing context.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks

Salahdine

Kaabouch

Ghazi

2017

Int J Communication

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Compressive sensing has been proposed as a low-cost solution for dynamic wideband spectrum sensing in cognitive radio networks. It aims to accelerate the acquisition process and minimize the hardware cost. It consists of directly acquiring a sparse signal in its compressed form that includes the maximum information using a minimum number of measurements and then recovering the original signal at the receiver. Over the last decade, a number of compressive sensing techniques have been proposed to enable scanning the wideband radio spectrum at or below the Nyquist rate. However, these techniques suffer from uncertainty due to random measurements, which degrades their performances. To enhance the compressive sensing efficiency, reduce the level of randomness, and handle uncertainty, signal sampling requires a fast, structured, and robust sampling matrix; and signal recovery requires an accurate and efficient reconstruction algorithm. In this paper, we proposed a method that addresses the previously mentioned problems by exploiting the Bayesian model strengths and the Toeplitz matrix structure. The proposed method was implemented and extensively tested. The simulation results were analyzed and compared to those of the 2 techniques: basis pursuit and orthogonal matching pursuit algorithms with Toeplitz and random matrix. To evaluate the efficiency of the proposed method, several metrics were used, namely, sampling time, sparsity, required number of measurements, recovery time, processing time, recovery error, signal-tonoise ratio, and mean square error. The results demonstrate the superiority of our proposed method over the 2 other techniques in speed, robustness, recovery success, and handling uncertainty.

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Techniques for dealing with uncertainty in cognitive radio networks

Cited by 29 publications

References 40 publications

A performance comparison of measurement matrices in compressive sensing

A performance comparison of measurement matrices in compressive sensing

Bayesian Compressive Sampling Based Wideband Spectrum Sensing in Cognitive Radio Network using Wavelet Transform

A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks

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