Low-complexity massive sparse signal reconstruction method

A sparse signal, low-complexity technology, applied in electrical components, code conversion, etc., can solve problems such as no practical value

Active Publication Date: 2015-07-08
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

At present, Bayesian-based recovery algorithms have relatively superior recovery performance. However, Bayesian-based recovery algorithms generally include matrix inversion processes, which are not practical in large-scale signal reconstruction problems.

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  • Low-complexity massive sparse signal reconstruction method
  • Low-complexity massive sparse signal reconstruction method
  • Low-complexity massive sparse signal reconstruction method

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Embodiment Construction

[0035] The technical solution of the present invention will be described in detail below in combination with the embodiments and the accompanying drawings.

[0036] The model that the present invention adopts:

[0037] In the process of signal acquisition, the signal is sampled based on compressed sensing technology. The scene can be a wireless broadband signal receiving end, or a video acquisition end in a scene such as video surveillance. The model can be described as y=Ax, and y is at the signal receiving end The data obtained by one sampling time slot (in image processing scenarios such as video surveillance, x is a matrix,

[0038] It can be converted into a vector form by vectorization operation, and y and A are transformed accordingly), y is an m-dimensional vector, x is an n-dimensional vector, and m1 ,...,x n ] T , y=[y1 ,...,y m ] T , Matrix A can be realized by PN pseudo-random code in hardware implementation.

[0039] Such as figure 1 As shown, a low-comple...

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Abstract

The invention belongs to the technical field of sparse signal restoration, and particularly relates to a low-complexity massive sparse signal reconstruction method in signal receiving on the basis of generalized approximate message passing (GAMP). The low-complexity massive sparse signal reconstruction method includes the following steps that a receiving signal y=A*x is obtained through compressed sensing and sampling, initialization is carried out, loop iteration is conducted, and restored signals are input. Compared with a traditional restoration algorithm based on Bayes, a generalized approximate message passing method is used. The low-complexity massive sparse signal reconstruction method can effectively reduce computation complexity on the basis of keeping signal superior reconstruction performance. Computation time complexity is reduced from O(n3) to O(mn), namely cube complexity is reduced to linear time complexity, and signal processing pressure of the rear end is greatly reduced, wherein m and n refer to the dimension of an observed value and the dimension of original signals.

Description

technical field [0001] The invention belongs to the technical field of sparse signal recovery, and in particular relates to a low-complexity block sparse signal reconstruction method based on Generalized Approximate Message Passing (GAMP) in signal reception. Background technique [0002] In the past few years, with the continuous increase of the signal bandwidth, in the radio frequency system, the requirement of the analog to digital converter (Analog to Digital Converter, AD) is getting higher and higher for the digitized signal. The higher the AD conversion rate, the greater the power consumption and the lower the number of effective bits. Recently, Compressed Sensing technology (Compressed Sensing), as a new technology for acquiring sparse signals at a low rate, has been widely researched and applied in academia and industry. Its application background is that the signal is sparse, that is, the signal has only a few sparse. For example, natural images and communication...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
Inventor 方俊张立造
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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