Stage-1 Precoding Matrix Determination for 3D Beamforming
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Solution Overview
Problem
In massive MIMO systems, the existing two-stage precoding structure fails to accurately match the channel state based on spatial features of user channels, leading to suboptimal channel capacity due to fixed vertical precoding, which limits the antenna downtilt adjustment and beamforming capabilities.
Innovation Solution
A method to determine a stage-1 precoding matrix based on spatial correlation matrix information fed back by terminals, enabling three-dimensional precoding by sending reference signals for each dimension and using codebook parameter information to estimate spatial correlations, allowing for adaptive beam pointing and improved cell-level spatial division.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed vertical precoding is used in stage-1, then device complexity is reduced and ease of operation is improved, but channel capacity and spectral efficiency deteriorate due to inability to accurately match channel state
Solution Approach 1:
The patent transforms the fixed vertical precoding into a dynamic precoding system that adapts to channel conditions. The base station sends reference signals for multiple dimensions (horizontal, vertical, cross-polarization), and the terminal feeds back spatial correlation information to enable dynamic determination of the stage-1 precoding matrix, allowing the system to adapt to varying spatial characteristics of user channels.
Solution Approach 2:
The patent changes the parameters used for precoding determination from fixed vertical-only parameters to multi-dimensional spatial correlation parameters. By introducing horizontal and cross-polarization dimensions alongside vertical dimension, the system enriches the parameter set for precoding matrix determination, enabling more accurate channel state matching and improved channel capacity.
2Productivity
If multi-dimensional reference signals are sent to enable three-dimensional precoding, then channel capacity and spectral efficiency are improved, but pilot overhead and measurement complexity increase
Solution Approach 1:
The patent segments the spatial correlation measurement process into multiple independent dimensions (horizontal, vertical, cross-polarization). Each dimension has its own reference signal and spatial correlation matrix, allowing the system to measure and process spatial characteristics separately for each dimension. This segmentation enables efficient resource allocation and reduces overall measurement complexity compared to a fully coupled multi-dimensional approach.
Solution Approach 2:
The patent implements partial three-dimensional precoding by selectively applying multi-dimensional spatial correlation measurement. Rather than requiring full three-dimensional measurement for all users and all time instances, the system can adaptively determine which dimensions to measure based on channel conditions, user locations, and system load, reducing pilot overhead while maintaining channel capacity benefits where needed.
Data Source
AI summary
Embodiments of this application disclose a precoding matrix determining method and apparatus, to determine a stage-1 precoding matrix based on indication information that is of a spatial correlation matrix and that is fed back by a terminal, thereby implementing three-dimensional precoding based on a channel state and increasing a channel capacity. The method includes: sending, by a base station, a plurality of groups of first reference signals, where the plurality of groups of first reference signals are in a one-to-one correspondence with a plurality of dimensions of an antenna array, and each of the plurality of groups of first reference signals is used by a terminal to estimate spatial correlation matrix information in a corresponding dimension; receiving the spatial correlation matrix information fed back by the terminal based on the plurality of groups of first reference signals; and determining a stage-1 precoding matrix based on the spatial correlation matrix information.