A channel estimation method for a large-scale MIMO system

A channel estimation and large-scale technology, applied in the transmission system, radio transmission system, baseband system components, etc., can solve the problem of high computational complexity, achieve the effect of reducing computational complexity and accurate estimated values

Inactive Publication Date: 2019-05-10
JIANGNAN UNIV
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Problems solved by technology

[0003] In order to solve the problem that the computational complexity of the channel estimation method becomes higher as the number of antennas increases in the MIMO system, the present invention provides a channel estimation method for the massive MIMO system. As the number of antennas increases, the computational complexity will not increase. Become more complex, and then can use existing equipment to complete channel estimation for massive MIMO systems

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  • A channel estimation method for a large-scale MIMO system
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Embodiment Construction

[0046] Such as Figure 1 ~ Figure 3 Shown, the present invention comprises a kind of channel estimation method of massive MIMO system, and it comprises the following steps:

[0047] S1: According to the MIMO system model Using the received signal vector r to construct the sample covariance matrix The sample covariance matrix Approximate substitutions are made according to the following formula:

[0048]

[0049] Among them: r(t) represents the received signal at the base station at time t, N represents the number of samples, r represents the vector of the received signal at the base station, p u Represents the signal-to-noise ratio, G represents the channel gain matrix, s represents the transmitted signal vector, and w represents the noise vector;

[0050] S2: Sample covariance matrix Based on the traditional SVD or EVD decomposition, the following formula can be obtained,

[0051]

[0052] Among them: matrix R can be decomposed into R=[R s , R n ],R s repre...

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Abstract

According to the channel estimation method for the large-scale MIMO system provided by the invention, with the increase of the number of antennas, the calculation complexity of the channel estimationmethod is not more complicated, and the channel estimation of the large-scale MIMO system can be completed by utilizing the existing equipment. The method comprises the following steps: S1, accordingto the MIMO system model, a received signal vector r is utilized to construct a sample covariance matrix; S2, on the basis of the sample covariance matrix (shown in the specification), the signal subspace RS is solved by adopting an FSCPI subspace tracking algorithm; S3, the pilot frequency sequence PHi is used to obtain the initial estimation of the channel gain matrix based on the pilot frequency; S4, solving a fuzzy matrix Ej by using the signal subspace RS and the initial estimation of the channel gain matrix (please see the formula in the specification); And S5: based on the signal subspace RS, Initial estimation of the fuzzy matrix Ej Initial estimation of the channel gain matrix is estimated again by means of an ILSP algorithm, and the final estimation of the channel gain matrix isobtained.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a channel estimation method for a massive MIMO system. Background technique [0002] The large-scale MIMO (Large Scale-Multiple-Input Multiple-Output, LS-MIMO) system is one of the key technologies of the fifth generation mobile communication system. By configuring a large number of antennas at the base station and the user end, the channel capacity of the system can be significantly improved. Data transfer rate, spectral efficiency and communication quality. Due to the increase in the number of antennas, many high-performance methods suitable for traditional MIMO systems are no longer suitable for massive MIMO systems, because these methods often generate high computational complexity. For example, in the semi-blind channel estimation algorithm, the subspace of the received signal is mainly obtained based on algorithms such as Singular Value Decomposition (SVD) o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/0413H04L25/02
Inventor 李正权周成赵小青倪鑫鑫梁金鹏刘汉旭刘洋吴琼李宝龙武贵路
Owner JIANGNAN UNIV
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