MU-MIMO Precoding Matrix Calculation for Fixed Beamforming Interference
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Solution Overview
Problem
In MU-MIMO systems that adopt fixed beamforming, existing technologies struggle to determine suitable precoding matrices, which are necessary for reducing interference between users and optimizing signal transmission.
Innovation Solution
A method is introduced to calculate precoding matrices by multiplying eigenvectors obtained from singular value decomposition of excluding channel matrices, specifically using a first eigenvector corresponding to the noise subspace and a second eigenvector corresponding to the signal subspace, to block-diagonalize the channel matrix and reduce interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If block-diagonalization of channel matrix is used to reduce interference in MU-MIMO, then interference reduction is achieved, but compatibility with fixed beamforming is lost
Solution Approach 1:
The patent merges block-diagonalization processing with fixed beamforming by integrating the precoding matrix determination into the existing fixed beamforming framework. The precoding matrix is determined based on the serving cell's channel matrix and interference cell's channel matrix while maintaining compatibility with the fixed beamforming structure, allowing both interference reduction and fixed beamforming to coexist.
Solution Approach 2:
The patent introduces an intermediary precoding matrix that bridges block-diagonalization and fixed beamforming. This precoding matrix serves as a mediator that applies block-diagonalization principles to reduce interference while preserving the fixed beamforming structure, enabling compatibility between the two approaches.
2Ease of operation
If precoding matrix is determined using existing methods, then processing is simplified, but interference reduction performance deteriorates in fixed beamforming MU-MIMO
Solution Approach 1:
The patent changes the parameters used for precoding matrix determination by incorporating both serving cell channel matrix information and interference cell channel matrix information. This parameter change enables the precoding matrix to simultaneously achieve interference reduction performance and maintain compatibility with fixed beamforming, resolving the contradiction between simplicity and effectiveness.
Data Source
AI summary
A communication control method includes: for each of plurality of receivers UE, calculating excluding channel matrices that include a plurality of equivalent channel matrices acquired by multiplying a transmit BF weight matrix corresponding to that receiver UE by each of a plurality of channel matrices corresponding to receivers UE other than that receiver UE, acquiring a first eigenvector that is included in a right-singular matrix obtained from single value decomposition of the excluding channel matrices, the first eigenvector corresponding to a noise subspace of the excluding channel matrices, acquiring a second eigenvector that is included in a right-singular matrix obtained from single value decomposition of a product of an equivalent channel matrix including the transmit BF weight matrix corresponding to that receiver UE and the first eigenvector, the second eigenvector corresponding to a signal subspace of the product, and obtaining a precoding matrix corresponding to that receiver UE by multiplying the first eigenvector by the second eigenvector.