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A method and device for sparsity estimation

A sparsity and estimated value technology, applied in the field of signal processing, can solve the problems of inability to estimate sparse signal sparsity and high total sampling overhead of the system, and achieve the effect of minimizing the total sampling overhead and accurate sparsity estimation

Inactive Publication Date: 2016-12-21
HUAWEI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0008] The purpose of the embodiments of the present invention is to provide a method for sparsity estimation, which aims to solve the problem of the inability to correctly estimate the sparseness of sparse signals and the large total sampling overhead of the system in scenarios with low signal-to-noise ratio and no prior information. The problem

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  • A method and device for sparsity estimation
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  • A method and device for sparsity estimation

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Experimental program
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Embodiment 1

[0065] figure 1 It shows the implementation flow chart of the sparsity estimation method provided by Embodiment 1 of the present invention, which is described in detail as follows:

[0066] In S101, calculate the number of sampling points required by the current iterative step;

[0067] In S102, according to the calculated number of sampling points and the preset scheduling policy, send sampling instructions to multiple terminal devices, so that the terminal devices perform sampling according to the sampling instructions to obtain sampling data, and the sampling instructions include : The number of sampling points that the terminal device needs to obtain and the random seed sequence used by the terminal device to generate the sampling matrix;

[0068] In S103, receiving the sampling data reported by each local terminal device as the sampling points required by the current iterative step obtained;

[0069] In this embodiment, the sampling instruction is sent to multiple termi...

Embodiment 2

[0076] The application scenario of the embodiment of the present invention may be a system composed of a base station and a plurality of local user terminals corresponding to the base station, where it is assumed that there are J local user terminals, and the method of sparsity estimation will be described below through the base station side and the user terminal side in the system , but the application scenario of the method for sparsity estimation in the embodiment of the present invention is not limited thereto, figure 2 It shows the implementation flow chart of the method for sparsity estimation provided by Embodiment 2 of the present invention, which is described in detail as follows:

[0077] In S201, parameter initialization;

[0078] In this embodiment, the initialized parameters include: the sparsity estimated value of the previous iteration step The number M of sampling points collected in all iteration steps before the current iteration step pt , after the base ...

Embodiment 3

[0123] image 3 It shows the implementation flow chart of the sparsity estimation method provided by Embodiment 3 of the present invention, which is described in detail as follows:

[0124] In S301, a sampling instruction sent by the system is received, and the sampling instruction includes: the number of sampling points to be acquired by the terminal device and a random seed sequence used by the terminal device to generate a sampling matrix.

[0125]In this embodiment, the sampling instruction may include: the number of sampling points that each local user terminal device needs to collect when performing low-speed sampling, and the random seed sequence used by each local user terminal device to generate a sampling matrix. Take the instruction received by a local user as an example, the instruction received is: the number of sampling points that the jth local user needs to collect when performing low-speed sampling is M r，j , and the random seed sequence used by the local use...

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Abstract

The present invention provides a method and device for sparsity estimation, which are applicable to the technical field of signal processing. The method includes: sending sampling instructions to multiple user terminal devices according to the calculated number of sampling points and a preset scheduling strategy, and obtaining The sampling points required for the current iteration step, based on the sampling points required for the current iteration step, the sampling points collected by all iteration steps before the current iteration step, and multiple observation vectors of the sparse signal to be observed, generate a mixed norm based on L1 / L2 and the objective function of multi-step cumulative minimum mean square error; according to the objective function, the joint sparsity estimation of the sparse signal to be observed is performed, and the result of the joint sparsity estimation is obtained; according to the result of the joint sparsity estimation, the joint sparseness degree estimates for termination judgments. The present invention makes it possible to effectively perform accurate sparsity estimation and minimize the total sampling overhead of the system under the actual application scene with poor signal-to-noise ratio and no prior information.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a method and device for sparsity estimation. Background technique [0002] In the existing technology, it is mostly assumed that the sparsity of the signal (the number of non-zero elements in the sparse coefficient vector) is known in advance, and according to the sparsity as a priori information to select the collection required to ensure the correct reconstruction of the original signal The number of sampling points of . However, in practical applications, the prior information of signal sparsity may be limited. For example, in the cognitive radio (Cognitive Radio, CR) broadband spectrum detection application scenario, since the sparsity of the broadband received signal at the CR user spectrum detection node directly depends on the spectrum occupancy of the authorized user (PU, Primary User), but due to There is usually no direct interaction information ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L1/00
Inventor 王悦
Owner HUAWEI TECH CO LTD