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Distributed compressive sensing sparsity adaptive reestablishment method

A compressed sensing and sparsity technology, applied in baseband systems, digital transmission systems, electrical components, etc., can solve problems such as difficulty in implementation, and achieve the effect of short running time and excellent performance

Inactive Publication Date: 2018-08-17
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Can distributed compressed sensing reconstruction algorithm be applied to channel estimation of dual-selection OFDM system? In response to this problem, some scholars have applied the DCS-SOMP algorithm to channel estimation, but the sparsity of the signal needs to be known, which is difficult to achieve in practice

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  • Distributed compressive sensing sparsity adaptive reestablishment method
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Embodiment Construction

[0034] The present invention discloses a distributed compressed sensing sparsity adaptive matching pursuit reconstruction (DCS-ImprovedSparsity Adaptive Matching Pursuit, DCS-IMSAMP) method, which is applied to time-frequency dual-selective OFDM system channel estimation. It contains three main technical problems, one is to transform the channel estimation problem of the dual-selection OFDM system into a distributed compressed sensing problem; the other is to improve the accuracy of the DCS-SAMP reconstruction algorithm; the third is to solve the long running time of the existing algorithm The problem. The implementation of these three parts will be introduced respectively below, and the beneficial effect of this algorithm on improving the channel estimation performance of the dual-selection OFDM system based on adaptive distributed compressed sensing will be illustrated through simulation.

[0035] (1) Distributed DCS channel model based on CE-BEM dual selection OFDM system ...

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Abstract

The invention discloses a distributed compressive sensing (DCS) improved sparsity adaptive matching pursuit (DSC-Improved Sparsity Adaptive Matching Pursuit, DCS-IMSAMP) reestablishment method. The estimation precision is improved by utilizing the joint sparsity of the signal and importing a dynamic threshold on the basis of the existing DSC sparsity adaptive matching pursuit (DCS-SAMP) algorithm;and the running time is saved by combining the clipping technology and the variable step length. The adaptivity can be ensured in the reestablishment process by using the algorithm, and the lower normalized mean square error (NMSE) and faster running speed can be acquired. The algorithm disclosed by the invention is applied to a channel estimation problem; compared with existing other algorithms,the excellent channel estimation effect can be obtained.

Description

technical field [0001] The present invention relates to the technical field of distributed compressed sensing for signal processing, in particular to the technical field of adaptive distributed compressed sensing reconstruction algorithm, specifically a distributed compressed sensing sparsity adaptive matching tracking reconstruction algorithm. Background technique [0002] In recent years, people have higher and higher requirements for information communication and data resource processing, and the traditional Nyquist sampling method can no longer meet the demand for data processing under high bandwidth. Compressive sensing (Compressive Sensing, CS) technology can complete data compression while collecting data, and the signal sampling rate based on CS is much lower than the traditional Nyquist sampling method. On the basis of CS, some scholars put forward the theory of Distributed Compressive Sensing (DCS). Using the joint sparsity between signals, DCS technology can join...

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Application Information

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IPC IPC(8): H04L25/02H03M7/30H04L27/26
CPCH03M7/3062H04L25/0242H04L25/0256H04L27/2601
Inventor 何雪云宋玉鸣
Owner NANJING UNIV OF POSTS & TELECOMM
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