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Low-complexity geometric mean value decomposition precoding implementation method based on bidiagonalization

A technology of geometric mean decomposition and implementation method, which is applied in radio transmission systems, electrical components, transmission systems, etc., and can solve the problem of high algorithm complexity

Active Publication Date: 2018-09-21
SOUTHEAST UNIV
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Problems solved by technology

This method has a stable number of iterations, but because it involves many complex matrix multiplication modules and CORDIC angle calculation modules, the algorithm complexity is still high

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  • Low-complexity geometric mean value decomposition precoding implementation method based on bidiagonalization
  • Low-complexity geometric mean value decomposition precoding implementation method based on bidiagonalization
  • Low-complexity geometric mean value decomposition precoding implementation method based on bidiagonalization

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Embodiment 1

[0057] Aiming at the problem of high complexity in implementing geometric mean value decomposition precoding in MIMO systems, the present invention proposes a method for realizing geometric mean value decomposition precoding based on a double diagonal matrix. First, solve the Hermitian matrix of the product of its conjugate transpose and its own matrix for the channel matrix, then use the properties of the Hermitian matrix to perform a bi-diagonal operation on the channel matrix, and then perform geometric mean decomposition on the solved bi-diagonal matrix to obtain the required The precoding matrix of . According to a preferred embodiment of the present invention, a geometric mean decomposition precoding algorithm applicable to specific implementations, the basic flow is as follows figure 1 As shown, the specific steps are:

[0058] Step 1: Calculate the conjugate transpose of the channel matrix and the product of itself, the channel matrix is The result of multiplying it...

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Abstract

The present invention provides a low-complexity geometric mean value decomposition precoding implementation method based on bidiagonalization. The method comprises the following steps: (1) calculatinga conjugate transpose of a channel matrix and own product; (2) based on a given Hermitian matrix bidiagonalization method, changing the channel matrix into a bidiagonal matrix through Givens rotation; (3) based on a given geometric mean value decomposition method, changing the bidiagonal matrix into an upper triangular matrix where the diagonal elements all equal to the geometric mean value of the channel matrix eigenvalues through Givens rotation; (4) constructing a precoding matrix with the geometric mean value decomposed, that is, the product of all Givens right rotation matrices. The technical scheme can determine the number of iterations, and uses the Hermitian matrix for solutions further to reduce the implementation complexity and reduce the use of the CORDIC (Coordinate Rotation Digital Computer) module. The method utilizes the properties of the Hermitian matrix to effectively reduce the implementation complexity of geometric mean value decomposition precoding based on bidiagonalization.

Description

technical field [0001] The invention relates to a method for implementing geometric mean value decomposition precoding based on double diagonalization, and belongs to the technical field of multi-user wireless communication. Background technique [0002] In MIMO systems, geometric mean decomposition precoding is a precoding method with better performance. Since it can decompose the channel into an upper triangular matrix whose diagonal elements are all eigenvalues ​​of the channel matrix, each spatial stream can have The same signal-to-noise ratio has greatly improved system performance. Not only for MIMO systems, but for multi-user MIMO systems, block diagonal precoding can be used to eliminate interference between users, and then geometric mean decomposition can be used to optimize the equivalent channel of each user to further improve system performance. Therefore, geometric mean decomposition precoding is widely used. [0003] Regarding the implementation of geometric ...

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Application Information

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IPC IPC(8): H04B7/0456H04B7/0452
CPCH04B7/0452H04B7/0456
Inventor 李春国王畑杨雅涵周童欣杨绿溪
Owner SOUTHEAST UNIV
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