Base Station Beamforming Matrix Composition With Partial Channel Estimation
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Solution Overview
Problem
In wireless communication systems, particularly in LTE and 5G NR, base stations face challenges in transmitter beamforming due to limited UE SRS switching capabilities and aggressive quantization in PMI feedback, leading to suboptimal beamforming performance.
Innovation Solution
A method and system that combine partial channel estimation from SRS switching with PMI feedback to compose a beamforming matrix, using singular value decomposition (SVD) and projection techniques to enhance beamforming accuracy, even with partial channel information and noisy feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SRS switching is used for uplink channel estimation, then beamforming capability is improved, but UE capability constraints limit the number of antenna ports that can be switched
Solution Approach 1:
The patent combines partial channel estimation from SRS switching with PMI feedback to compose a complete beamforming matrix. This merging approach allows the system to overcome UE capability constraints by integrating multiple information sources rather than relying solely on SRS switching, thereby achieving accurate channel estimation without requiring full SRS switching capability at the UE.
Solution Approach 2:
The patent uses partial channel estimation from limited SRS switching (partial action) rather than requiring complete SRS switching across all antenna ports. By combining this partial estimation with PMI feedback, the system achieves the necessary beamforming performance without demanding full SRS switching capability from the UE, thus adapting to UE capability constraints.
2Loss of information
If PMI codebook size is limited due to signaling overhead constraints, then feedback overhead is reduced, but beamforming precision deteriorates due to aggressive quantization
Solution Approach 1:
The patent merges partial channel estimation (which provides continuous channel information) with PMI feedback (which provides quantized precoding information) to compose the beamforming matrix. This combination allows the system to maintain beamforming precision by supplementing the quantized PMI information with more accurate partial channel estimation, thereby compensating for the information loss due to codebook quantization while keeping feedback overhead low.
Solution Approach 2:
The partial channel estimation acts as an intermediary that bridges the gap between limited PMI codebook resolution and the need for accurate beamforming. By using partial channel estimation to supplement the quantized PMI information, the system recovers some of the precision lost due to aggressive quantization without increasing the PMI codebook size or feedback overhead.
3Measurement precision
If complete channel estimation is performed, then beamforming accuracy is improved, but system complexity and signaling overhead increase
Solution Approach 1:
The patent uses partial channel estimation from SRS switching rather than complete channel estimation across all antenna ports. This partial action approach, when combined with PMI feedback, achieves sufficient beamforming accuracy for practical applications while significantly reducing the complexity and signaling overhead associated with obtaining complete channel information from all UE antenna ports.
Solution Approach 2:
The patent combines partial channel estimation with PMI feedback to achieve complete beamforming matrix construction without requiring complete channel estimation. This merging approach allows the system to achieve the necessary beamforming accuracy using only partial channel information supplemented by PMI, thereby reducing the complexity and signaling overhead that would be required for complete channel estimation.
Data Source
AI summary
Methods and systems of obtaining a beamforming matrix, the method comprising inputting PMI feedback from a user equipment (UE), inputting partial channel estimation derived from sounding reference signal (SRS) switching, and composing a precoding matrix using the PMI feedback and partial channel estimation.