A novel cooperative spectrum sensing scheme based on virtual soft information fusion

Wang, Ben; Hu, Pan; Gai, Yibing; Peng, Jun; Ye, Jin; Zhang, Yinghai

doi:10.1016/s1005-8885(11)60472-x

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…In addition to the above technical solutions, researchers are also trying to study broadband spectrum sensing from different directions: literature [36] through the spectrum sensing method driven by image denoising and image classification ideas, to solve the problems of high computing complexity of spectrum sensing method and low accuracy in strong noise environment. Literature [37] improves the problem of unefficient processing of high-dimensional signals through an ultra-broadband compressed sensing method based on approximate message transfer. Overall, there is still plenty of room for improvement in broadband sensing technology.…”

Section: Broadband Spectrum Sensingmentioning

confidence: 99%

“…These devices need to operate on a wide frequency band, and require a high-speed processing unit to calculate the signal processing task within a relatively short time delay. Moreover, the process of cognitive radio spectrum sensing requires high sampling rate, high analytical ability of analog-to-digital conversion, and support [37] of high-speed signal processors.…”

Section: Hardware Requirementsmentioning

confidence: 99%

“…During the sensing time, it cannot affect the normal communication of the main user, but also requires the throughput of the secondary user to reach the maximum, which brings certain limitations to the spectrum sensing algorithm and brings great challenges to the design of cognitive radio. Therefore, the selection of spectrum sensing parameters requires comprehensive consideration of system sensitivity, accuracy and reliability, and other performance indicators to obtain the appropriate sensing time and enable the cognitive network system to achieve the desired effect [37].…”

Section: Perception Of Timementioning

confidence: 99%

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Application analysis of spectrum sensing technology

Sun,

Zhang

2024

Advanced Fiber Laser Conference (AFL2023)

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With the rapid increase in the number of mobile users and the continuous update of wireless communication technology, data traffic has experienced a massive growth, and the demand for radio spectrum has also risen. The increasing demand for spectrum leads to the shortage of spectrum resources, and the current spectrum utilization method is relatively inefficient. In order to improve the utilization efficiency of spectrum resources and relieve the pressure of resource shortage, cognitive radio technology comes into being. This technology mainly improves spectrum utilization and communication efficiency through intelligent adaptive spectrum sensing and adjustment, among which spectrum sensing is its core content. This paper summarizes the research of spectrum sensing technology, mainly from the basic principles, models, current problems and future development direction are discussed.

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Section: Broadband Spectrum Sensingmentioning

confidence: 99%

Section: Hardware Requirementsmentioning

confidence: 99%

Section: Perception Of Timementioning

confidence: 99%

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Application analysis of spectrum sensing technology

Sun,

Zhang

2024

Advanced Fiber Laser Conference (AFL2023)

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show abstract

“…M. G. Kang, et al, studied an image restoration method based on constrained least squares, which took into account the local statistical characteristics, and achieved good results [5]. Y. Wang, et al, proposed an adaptive selection method of regularization parameter in image restoration, to ensure the restoration result is the global optimal solution, and keep good details of restored image [6]. H. Zhang, et al, presented constrained least squares iteration restoration method based on pre-filtering (PF-CLSI) for CBCT projected images restoration [7].…”

Section: Intorductionmentioning

confidence: 99%

Projected Image Restoration Method Based on Genetic Algorithm for Cone-Beam CT

Huang

Zhang

et al. 2013

AMM

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For the problem of image quality degradation in cone-beam CT (CBCT) based on flat panel detector (FPD), a genetic algorithm based on pre-segmentation (PS-GA) is proposed for CBCT projected image restoration. According to the characteristic of that most of the area of the projected image is empty and without the tested object, a robust segmentation algorithm is used in this method to segment the smallest rectangle that contains the tested object, and the calculating range is limited to the smallest rectangle by the specially designed genetic algorithm, which significantly reduced the amount of calculated data. The experimental results show that the method raised the edge sharpness, contrast-to-noise ratio (CNR) and average gradient (AG) of the projected images and slice images, and there is no visible artifacts introduced.

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“…Kang, et al, studied an image restoration method based on constrained least squares, which took into account the local statistical characteristics, and achieved good results [4]. Y. Wang, et al, proposed an adaptive selection of regularization parameter in image restoration, to ensure the restoration result is the global optimal solution, and keep good details of restored image [5].…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Method Based on Pre-Filtering for Cone-Beam Computed Tomography

Zhang

Shi

Huang

et al. 2012

AMM

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For the problem of image quality degradation of cone-beam Computed Tomography (CBCT) based on flat panel detector (FPD), a constrained least squares iteration (CLSI) restoration method based on pre-filtering is proposed. Firstly, the original projected images are denoised with bilateral filtering algorithm. Then, the denoised projected images are restored with CLSI. Finally, the final restored images are obtained by adding the noise images, which got by subtracting the projected images before and after denoising, to the restored images. The experimental results show that the method well inhibits the noise amplification phenomenon in image restoration, and increases the edge sharpness and contrast-to-noise ratio (CNR) of the projected images and slice images. The CBCT image quality is significantly improved with this method.

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A novel cooperative spectrum sensing scheme based on virtual soft information fusion

Cited by 4 publications

References 12 publications

Application analysis of spectrum sensing technology

Application analysis of spectrum sensing technology

Projected Image Restoration Method Based on Genetic Algorithm for Cone-Beam CT

Image Restoration Method Based on Pre-Filtering for Cone-Beam Computed Tomography

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