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A Method and Device for Channel Estimation and Pilot Optimization Based on Compressed Sensing

A technology of channel estimation and compressed sensing, which is applied in the field of channel estimation and pilot optimization based on compressed sensing, can solve the problems of poor optimization performance and large demand for pilots, reduce the block correlation value, accelerate the convergence speed, and reduce the The effect of frequency demand

Active Publication Date: 2020-12-22
HENAN UNIVERSITY OF TECHNOLOGY
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to provide a channel estimation pilot optimization method and device based on compressed sensing, which is used to solve the problems of large pilot demand and poor optimization performance in the prior art, improve pilot optimization performance, and make channel estimation more accurate

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  • A Method and Device for Channel Estimation and Pilot Optimization Based on Compressed Sensing
  • A Method and Device for Channel Estimation and Pilot Optimization Based on Compressed Sensing
  • A Method and Device for Channel Estimation and Pilot Optimization Based on Compressed Sensing

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Embodiment 1

[0056] When the compressed sensing channel estimation method using the sparse characteristics of the massive MIMO channel estimates the channel state information, the pilot position needs to be optimized, and the following methods are used:

[0057] 1) According to the fact that the pilot signals sent by all antennas have the same pilot position and pilot power, the block correlation value of the known measurement matrix is ​​simplified, and the objective function of pilot optimization with the minimum block correlation value is established.

[0058] The simplification process includes the following steps:

[0059] Step 1, consider a massive MIMO system, the base station has M antennas serving multiple single-antenna users. Then the channel impulse response from the mth antenna of the base station to a certain user is h m =[h m (0), h m (1),...,h m (L-1)] T , where L is the channel length, since the channel is sparse, so h m There are only K non-zero taps in , and Kp sub...

Embodiment 2

[0120] The optimization problem of the pilot position can be expressed by the objective function with the smallest block correlation value:

[0121]

[0122]Where, r=l-k, the formation process of the above objective function belongs to the prior art, and the specific journal name is "Pilot Allocation for MIMO-OFDM Systems: A Structured Compressive Sensing Perspective".

[0123] Pilot optimization using an iterative approach:

[0124] First, set the number of outer loops M 1 and the number of inner loops M 2 .

[0125] In each outer loop, randomly generate T (1≤T≤N P ) pilot positions, and then add a pilot position in each iteration according to the principle of minimum block correlation value until N P Pilot positions stop iterating, and set the obtained pilot positions as the initial value of the inner loop.

[0126] In each inner loop, it is assumed that the pilot position set generated by the latest iteration is p, for k=1,...,N P , select the element with the sm...

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Abstract

The invention relates to a channel estimation pilot frequency optimization method and device based on compressed sensing, and relates to the field of broadband wireless communication. Channels betweendifferent antennas are modeled as a joint sparse signal, parameter estimation of a large-scale MIMO channel is constructed as a sparse signal reconstruction problem in structural compressed sensing,and a pilot frequency position set is updated based on the pilot frequency optimization criterion of a minimum block related value, thereby improving the estimation accuracy of the large-scale MIMO channel.

Description

technical field [0001] The invention belongs to the field of broadband wireless communication, and in particular relates to a channel estimation pilot optimization method and device based on compressed sensing. Background technique [0002] Massive MIMO (Multiple Input Multiple Output) system greatly improves the spectrum efficiency and energy efficiency of the communication system by configuring a large number of antennas in the base station. As a key technology of 5G communication, it has attracted extensive attention from scholars at home and abroad. . But massive MIMO also faces many challenges in practice. For example, obtaining accurate channel state information (CSI, Channel Condition Information) is crucial to massive MIMO systems, and most current channel estimation methods use pilots to obtain CSI. As the number of base station antennas increases, The number of channels to be estimated increases, resulting in a large amount of pilot overhead. [0003] In order t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02H04L5/00
CPCH04L5/0048H04L25/0204H04L25/0216H04L25/0224H04L25/0226
Inventor 杨铁军吕倬凯朱春华杨静傅洪亮樊超
Owner HENAN UNIVERSITY OF TECHNOLOGY
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