Multi-signal reconstruction method

A multi-signal and signal technology, applied in the field of wireless communication, can solve problems such as large matrix order, increased computational complexity, and difficulty in application and promotion

Inactive Publication Date: 2014-07-23
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0007] The algorithm using eigenbasis combined with Bayesian compressed sensing needs to invert the matrix. In particular, the eigenexpansion basis needs to use the statistical characteristics of the signal to improve the recovery ability of the signal. The length of the signal is relatively long, and the order of the matrix will be Relatively large, the inversion operation will greatly increase the complexity of the operation, making it very difficult to apply and promote. Therefore, it is an innovative and important practical significance to study a low-complexity improved Bayesian compressed sensing algorithm. challenging task

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[0034] Suppose there are D (D is an integer greater than or equal to 1) signals: Divide each of these multiple original signals into L segments, each segment has the same length and is an integer greater than 1, each segment of any signal can be completely represented by the same complete base and different expansion coefficients, each segment Corresponding to different expansion coefficients, use a special complete base Ψ to make the D signals sparse in this complete set, that is, most of the expansion coefficients are 0 or close to 0, and Ψ is an orthogonal square matrix composed of characteristic basis vectors, We just take N of Ψ e The main column vectors constitute the subspace of Ψ, denoted as Ψ', thus constructing the matrix consider in Ψ S Expansion coefficients on ; for each signal filter, let Each segment of Ψ is projected on the subspace Ψ' of Ψ, and the filtered signal is denoted as (i=1,2,...,D) is in Ψ S Expansion coefficient under ; L is an integer ...

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Abstract

The invention belongs to the technical field of wireless communication, particularly relates to the improved low-complexity Bayesian algorithm adopting compressed sensing to combine and recover a plurality of signals in an ultra-wide-band communication system, and provides a method for combining and reconstructing the signals through the improved low-complexity Bayesian compressed sensing algorithm in the wireless communication system. According to the method, the received signals are segmented and filtered, different measurement matrixes are used for carrying out linear recombination on each filtered signal, after a series of low-complexity iterative operations utilizing the correlation among the signals, the expansion coefficient of each original signal under the same feature basis can be measured, and therefore each original signal can be reconstructed more accurately.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for reconstructing multiple signals by using a low-complexity improved Bayesian compressed sensing algorithm in an ultra-wideband communication system. Background technique [0002] Since UWB (Ultra Wideband) signals are mostly narrow pulse signals in the time domain, their frequency domain bandwidth is too wide. According to the requirements of the Nyquist sampling law, the sampling rate will be as high as several GHz, so that the analog-to-digital converter The design brings huge challenges, and a large amount of data processing brings a great burden to the digital signal processor, the current hardware level is difficult to achieve the above requirements. Compressed sensing (CS, Compressed sensing), as a novel low-speed signal sampling theory, breaks through the limitation of Nyquist sampling law. Some literatures have carried out theoretica...

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Application Information

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IPC IPC(8): H03M7/30
Inventor 王梦瑶成先涛袁波岳光荣李少谦
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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