Fast matching pursuit for wideband spectrum sensing in cognitive radio networks

Abdel-Sayed, Michael M.; Khattab, Ahmed; Abu-Elyazeed, Mohamed F.

doi:10.1007/s11276-017-1545-7

Cited by 7 publications

(10 citation statements)

References 42 publications

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“…In this algorithm, the sensing matrix

Φ

will be taken as the matrix

A

given by (4). This contrasts with [16] in which we applied the FMP concepts to reconstruct a wideband frequency spectrum from time domain samples for cognitive radio applications. The FMP algorithm then iteratively identifies the support of the sparse signal by adaptively selecting elements from a reduced set of the correlation values.…”

Section: Fmp‐based Classificationmentioning

confidence: 94%

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Fast matching pursuit for sparse representation‐based face recognition

2018

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Even though face recognition is one of the most studied pattern recognition problems, most existing solutions still lack efficiency and high speed. Here, the authors present a new framework for face recognition which is efficient, fast, and robust against variations of illumination, expression, and pose. For feature extraction, the authors propose extracting Gabor features in order to be resilient to variations in illumination, facial expressions, and pose. In contrast to the related literature, the authors then apply supervised locality‐preserving projections (SLPP) with heat kernel weights. The authors’ feature extraction approach achieves a higher recognition rate compared to both traditional unsupervised LPP and SLPP with constant weights. For classification, the authors use the recently proposed sparse representation‐based classification (SRC). However, instead of performing SRC using the computationally expensive ℓ1 minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors’ proposed framework achieves ∼99% recognition rate using four benchmark face databases, significantly faster than related frameworks.

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“…In this algorithm, the sensing matrix

Φ

will be taken as the matrix

A

Section: Fmp‐based Classificationmentioning

confidence: 94%

“…For classification, we apply SRC previously proposed in [8]. In contrast to [8], which uses

ℓ_{1}

minimisation for CS recovery, we propose applying our fast matching pursuit (FMP) algorithm [16]. This results in very efficient, fast, and accurate recognition, suitable for real‐time applications, compared to other recovery algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fast matching pursuit for sparse representation‐based face recognition

2018

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“…Here

B^{- 1} = false({boldΦ}_{i - 1}^{normalT} {boldΦ}_{i - 1} false)^{- 1}

is available from the last iteration and is only calculated directly in the first iteration. The complete details are presented in [6].…”

Section: Simultaneous Fast Matching Pursuitmentioning

confidence: 99%

“…However, since

ℓ_{1}

norm minimisation is computationally expensive, various greedy recovery algorithms have been proposed for CS reconstruction, aiming to reduce reconstruction complexity without affecting reconstruction accuracy. Greedy algorithms include orthogonal matching pursuit (OMP) [3], compressive sampling matching pursuit (CoSaMP) [4], adaptive reduced‐set matching pursuit (ARMP) [5] and fast matching pursuit (FMP) [6]. Such algorithms iteratively find the support of the sparse vector, and then estimate the vector based on this support.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous fast joint sparse recovery for WSN and IoT applications

Melek

Khattab

Abu-Elyazeed

2020

IET wirel. sens. syst.

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Joint sparse recovery aims to recover a number of sparse signals having joint sparsity from multiple compressed measurements. Such a problem is finding increasing applications in wireless sensor networks (WSN) and Internet of Things (IoT) where multiple sensors collect measurements. However, existing algorithms lack both reconstruction accuracy and speed at the same time. In this study, the authors propose a joint sparse recovery algorithm, simultaneous fast matching pursuit (SFMP), which exploits some of the concepts developed for the fast matching pursuit (FMP) algorithm. SFMP achieves significant improvement in reconstruction time and speed compared to other related existing algorithms. In contrast to related algorithms, support selection is performed efficiently as the number of selected atoms is adapted from an iteration to another. Furthermore, signal estimation is performed avoiding large matrix inversion as in related algorithms. Moreover, simultaneously pruning the estimated signals, results in removing incorrectly selected ones. Due to the efficient selection strategy and the simultaneous pruning operation, the algorithm shows significant improvement in reconstruction accuracy from noisy measurements. SFMP achieves significant speed improvement over simultaneous orthogonal matching pursuit, and significant accuracy improvement over simultaneous compressive sampling matching pursuit, requiring a much smaller number of measurements.

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“…Several techniques based on reconstruction algorithms have been widely recommended and proposed in the literature [29][30][31][32]. For instance, in [29] [30], the authors presented the Wavelet Packet Adaptive Reduced-set Matching Pursuit (WP-ARMP) as a new approach and suitable Greedy recovery algorithm for compressive spectrum sensing. The basic idea of this technique is based on a developed Fast Matching Pursuit (FMP) algorithm that practically allows a recovery rate of the input signal at 25% of the Nyquist rate.…”

Section: Approaches Based On Reconstruction Modelmentioning

confidence: 99%

A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks

Benazzouza

Ridouani

Salahdine

et al. 2019

2019 IEEE International Smart Cities Conference (ISC2)

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Spectrum sensing aims at searching and finding the unused frequency bands in specific radio spectrum. It monitors the frequency bands to detect the activity of primary/licensed users and decide if secondary users can use these bands or not. In order to improve the efficiency of spectrum sensing in wideband cognitive radio networks, compressive sensing framework has been recommended and studied in many papers since it helps the system to get better and faster results using the sparse structure of the radio spectrum. Therefore, this paper represents an in-depth survey of the best requirements of compressive sensing and spectrum sensing techniques for robust combination and effective solution for wideband cognitive radio networks. It also provides examples of innovative applications of compressive spectrum sensing including IoT, smart city and 5th generation of mobile networks. To sum up some challenges and research directions related to compressive spectrum sensing technique are given at the end.

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Fast matching pursuit for wideband spectrum sensing in cognitive radio networks

Cited by 7 publications

References 42 publications

Fast matching pursuit for sparse representation‐based face recognition

Fast matching pursuit for sparse representation‐based face recognition

Simultaneous fast joint sparse recovery for WSN and IoT applications

A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks

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