An Efficient Greedy Algorithm for Wide Band Spectrum Sensing in Cognitive Radio Networks

Nasar, Wajeeha; Maud, Abdur Rahman; Wang, Hao; Dia, Hong-Ning

doi:10.1109/cybermatics_2018.2018.00072

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The performance of CS based on the proposed sensing matrix is measured by computing the absolute error and MSE (mean square error). The absolute error is defined by Nasar et al 33 All the following results of the proposed matrix are based on x (0) = 0.5, a = 4.1, and r = 12 of CQAC map.

Abserr goodbreak= \frac{\sum_{i = 1}^{K} recovered signal - original signal}{Length of original signal}

The MSE is defined by 21

italicMSE goodbreak= \frac{1}{N} \sum_{N} {(original signal - recovered signal)}^{2}

To evaluate the performance of the CS based on CQAC sensing matrix, we compare it with Gaussian matrix and chaotic sensing matrices based on logistic and quadratic maps in reference 13 and chaotic matrix based on Chebyshev map in reference, 20 which is based on the modified chaotic map by Equations and , respectively:

v_{m} goodbreak= 1 goodbreak- 2 x_{1000 + italicmd}

v_{m} goodbreak= C_{1000 + italicmd}

where m = 0, 1, 2 …, d is the down sampling factor, x is the sequence created from logistic and quadratic maps, and C is the sequence created from Chebyshev map.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Compressive sensing based on novel chaotic matrix for cognitive radio

Abed

Abdullah

2022

Int J Communication

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Summary Cognitive radio (CR) is an emerging technology for optimum spectrum utilization. Spectrum sensing (SS) is the first step of CR. Important challenge in the next generations is wideband SS. Compressive sensing (CS) can be used as an SS in CR to solve this challenge. Sensing matrix (SM) plays an important role in CS. For better performance, SM must have low mutual coherence. The existing matrices in the literature are random and chaotic. All random matrices have a good performance, but they have problems of large memory storage, complexity, sharing bandwidth, and low security. Chaotic matrices (CMs) are the best but still have some problems including the use of sample distance in chaotic sequence that consumes high storage resources, low performance and compression under low signal‐to‐noise ratio (SNR), and low security. In this paper, we propose a new 1‐D chaotic map for constructing a novel SM. The proposed map has simple structure, wide range chaotic behavior, and high security. Its chaotic behavior is confirmed using Lyapunov exponent, bifurcation, and trajectory. The CS performance based on novel CM is measured using absolute error ( Abserrtrue), mutual coherence, memory cost, computational complexity, and MSE (mean square error), while evaluated through comparisons with matrices in the literature. The simulation and mathematical results show that the proposed map is a hyperchaotic with efficient chaotic behavior in wide range of parameters and low memory storage and complexity. The results also show that it has low Abserr, MSE, and mutual coherence at low SNR with high compression.

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Abserr goodbreak= \frac{\sum_{i = 1}^{K} recovered signal - original signal}{Length of original signal}

The MSE is defined by 21

italicMSE goodbreak= \frac{1}{N} \sum_{N} {(original signal - recovered signal)}^{2}

v_{m} goodbreak= 1 goodbreak- 2 x_{1000 + italicmd}

v_{m} goodbreak= C_{1000 + italicmd}

where m = 0, 1, 2 …, d is the down sampling factor, x is the sequence created from logistic and quadratic maps, and C is the sequence created from Chebyshev map.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Compressive sensing based on novel chaotic matrix for cognitive radio

Abed

Abdullah

2022

Int J Communication

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“…Most compressive wideband sensing approaches need spectrum recovery, either signal recovery [1][2][3] or power spectrum recovery [4,[7][8]. However, the above approaches that use highly non-linear reconstruction methods for spectrum recovery are computationally expensive.…”

Section: A Motivationmentioning

confidence: 99%

“…Since then, CS-based wideband spectrum sensing method has become a research hotspot. The most classical methods fall into two broad categories: nonconvex optimization-based [2] and greedy pursuit-based [3]. However, CS-based wideband spectrum sensing requires that the wideband spectrum satisfies sparsity.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Compressive Wideband Spectrum Sensing Algorithm Based on Least Squares Support Vector Machine

Ren

Chen

et al. 2021

IEEE Access

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Most of the compressive wideband spectrum sensing algorithms need to recover the spectrum, which require high computational complexity. Recently, a novel algorithm for compressive wideband sensing without spectrum recovery (NoR) was proposed. Its computational complexity is several orders of magnitude less than that of algorithms that need spectrum recovery. However, enabling by structureconstrained assumption of sparse spectrum, NoR may fail. In order to expand its scope of application while reducing the computational complexity as much as possible, we propose an adaptive sensing (ADP) algorithm that is a powerful hybrid of the no recovery and partial recovery (PR) algorithms. The ADP algorithm adaptively chooses the no recovery or partial recovery scheme depending on the situation learned by the least squares support vector machine (LS-SVM). By simulation and analysis, compared with NoR, PR and another excellent algorithm (orthogonal matching pursuit), the ADP suits better for practical applications.

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“…Because compressed sensing (CS) was first applied to wideband spectrum sensing (WSS) [1], sub-Nyquist sampling-based WSS methods have received a lot of attention from experts, and the development momentum has been unstoppable. Some fairly classical CS-based methods, such as those non-convex optimization-based [2] and greedy pursuit-based [3], although they are good solutions to the problem of high Nyquist sampling rate for WSS, they require signal recovery, which brings a large computational burden. To overcome the disadvantage of high computational complexity of the CS-based method, some later works propose reconstructing the power spectrum [4] or covariance matrix [5] of the wideband signal from sub-Nyquist samples obtained by the multichannel sampling scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we choose two additional algorithms for comparison. One is the orthogonal matching pursuit (OMP) algorithm [3], a representative of CS-based methods, and the other is the NoR algorithm. Experimental simulations and computational complexity analysis prove that when the spectrum is not sparse, the AdNoR not only has much better detection performance than the other three algorithms but also has a low computational complexity comparable to that of the NoR and ADP algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum

Ren

Chen

et al. 2022

Sensors

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The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt well to non-sparse spectrum scenarios. The algorithm continues to implement the idea of our previously proposed "no reconstruction (NoR) of spectrum" algorithm, thus having low computational complexity. The new one is actually an advanced version of the NoR algorithm, so it is called AdNoR. The key to its advancement lies in the establishment of a folded time-frequency (TF) spectrum model with the same special structure as in the fold spectrum model of the NoR algorithm. For this purpose, we have designed a comprehensive sampling technique which consists of multicoset sampling, digital fractional delay, and TF transform. It is verified by simulation that the AdNoR algorithm maintains a good sensing performance with low computational complexity in the non-sparse scenario.

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An Efficient Greedy Algorithm for Wide Band Spectrum Sensing in Cognitive Radio Networks

Cited by 6 publications

References 12 publications

Compressive sensing based on novel chaotic matrix for cognitive radio

Compressive sensing based on novel chaotic matrix for cognitive radio

An Adaptive Compressive Wideband Spectrum Sensing Algorithm Based on Least Squares Support Vector Machine

A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum

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