Covariance Matrix Feedback Compression for NR Beamforming
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Solution Overview
Problem
Current wireless communication systems face increased signaling overhead due to the growing number of antenna ports, particularly in explicit feedback mechanisms like covariance matrix feedback, which becomes cumbersome as the number of antenna ports increases, affecting the efficiency of beamforming in Next Generation NodeBs (gNBs).
Innovation Solution
A technique where a User Equipment (UE) feeds back only the M best ranking entries of the covariance matrix to the gNB, allowing the base station to reconstruct and perform beamforming, thereby reducing signaling overhead through quantization and compression of the covariance matrix using a Type I feedback scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the complete covariance matrix is fed back from UE to gNB, then beamforming performance is maintained, but signaling overhead increases significantly
Solution Approach 1:
The patent extracts only the essential components of the covariance matrix (e.g., dominant eigenvectors or diagonal elements) for feedback, rather than transmitting the complete matrix. This extraction principle reduces the feedback data volume while preserving the most critical information needed for beamforming operations at the gNB.
Solution Approach 2:
The patent transforms the covariance matrix representation by changing its parameters - converting it to an alternative form such as eigenvector-based representation or diagonal element selection. This parameter transformation maintains the essential beamforming information while significantly reducing the number of bits required for feedback.
2Productivity
If the number of antenna ports increases, then system capacity and throughput are improved, but covariance matrix feedback complexity increases
Solution Approach 1:
As the number of antenna ports increases, the patent applies extraction by selecting only the most significant components of the covariance matrix (such as top-K eigenvectors or dominant diagonal elements). This approach maintains beamforming accuracy for high-capacity systems while preventing feedback complexity from scaling linearly with the number of antenna ports.
Solution Approach 2:
The patent changes the representation parameters of the covariance matrix to adapt to increased antenna ports. By transforming the matrix into a compact form (e.g., eigen-decomposition based representation), the system can handle larger antenna arrays without proportionally increasing feedback overhead.
3Quantity of substance
If quantization of covariance matrix is applied, then signaling overhead is reduced, but feedback precision decreases
Solution Approach 1:
The patent applies parameter changes by transforming the covariance matrix into an alternative representation (such as eigenvector-based or diagonal form) before quantization. This transformation concentrates the essential information into fewer parameters, allowing for effective quantization with reduced precision loss while achieving significant overhead reduction.
Data Source
AI summary
Technology for a user equipment (UE) operable to assist a Next Generation NodeB (gNB) for beamforming is disclosed. The UE can determine a covariance matrix for a channel between the UE and the gNB. The UE can quantize the covariance matrix to obtain a quantized covariance matrix. The quantized covariance matrix can include M best diagonal entries that are selected from the covariance matrix, wherein M is an integer. The UE can encode the quantized covariance matrix as feedback for transmission to the gNB.