FDD MIMO Beamforming Using Multi-Angle Covariance Transformation
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Solution Overview
Problem
Current FDD MIMO products face performance limitations due to incomplete utilization of partial reciprocity in channel covariance transformation, particularly when multiple dominant angles of arrival are present, leading to degraded performance from scattering and angular spread.
Innovation Solution
A beamforming solution that considers multiple dominant angles of arrival to transform channel covariance matrices, using a threshold to determine significant angles and applying accumulative transformations to enhance robustness against angular spread and imperfect reciprocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If channel reciprocity is utilized in FDD for MIMO enhancements, then performance improvement is achieved, but the utilization is incomplete and needs optimization
Solution Approach 1:
The patent segments the channel covariance transformation process by identifying and processing multiple dominant angles of arrival separately. Instead of treating the channel as a single entity, it divides the transformation into multiple angular components, each handled through separate beamforming vectors, thereby completing the reciprocity utilization across different spatial directions
Solution Approach 2:
The patent introduces angular spread as an additional dimension for channel transformation. By considering multiple angles of arrival rather than a single angle, it transforms the channel covariance matrix across the angular dimension, enabling more complete exploitation of spatial reciprocity in FDD MIMO systems
2Device complexity
If a single dominant angle is used for channel covariance transformation, then processing is simplified, but performance degrades due to scattering and angular spread
Solution Approach 1:
The patent segments the single-angle transformation into multiple angle-specific transformations. Each dominant angle of arrival is processed separately with its own beamforming vector, maintaining manageable complexity for each individual angle while collectively achieving superior performance under angular spread conditions
Solution Approach 2:
The patent applies local quality by optimizing the transformation for each specific angle of arrival individually. Rather than using a uniform single-angle approach, it tailors the beamforming vectors to each dominant angle's local channel characteristics, thereby maintaining high performance across the entire angular spread
3Measurement precision
If CSI feedback from terminal device is used for DL beamforming design, then accurate beamforming is achieved, but overhead becomes very large
Solution Approach 1:
The patent introduces channel covariance matrices as an intermediary representation that captures essential channel statistics without requiring full CSI feedback. By transforming uplink channel covariance to downlink channel covariance through dominant angle analysis, it achieves accurate beamforming while significantly reducing feedback overhead compared to direct CSI reporting
Data Source
AI summary
Embodiments of the present disclosure relate to a beamforming solution for FDD MIMO communication. A first device receives a reference signal from a second device in a first channel, and determines a covariance matrix associated with the first channel. The first device determines a set of beamforming vectors from the covariance matrix based on the number of dominant angles of arrival associated with the reference signal and the number of beams associated with the set of beamforming vectors, and performs a transmission to the second device based on the set of beamforming vectors in a second channel. In this way, beamforming vectors can be calculated from the transformed channel covariance matrix with the angular spread and imperfect reciprocity considered.