Precoding Matrix Indicator Compression for Uplink Overhead Reduction
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Solution Overview
Problem
Current beamforming techniques in wireless communication systems face challenges in reducing uplink overhead while maintaining the effectiveness of precoding information, as compressing this information can lead to reduced accuracy and increased error rates in signal transmission.
Innovation Solution
The method involves determining a target component of precoding information and a decompressed component, using iterative operations like gradient descent to optimize scaling and rotation coefficients, and applying compression techniques such as spatial and frequency compression to reduce data sent from user equipment to the base station, thereby improving the correlation and effectiveness of the precoding matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If precoding information is compressed to reduce uplink overhead, then the amount of data transmitted is reduced, but the accuracy and effectiveness of precoding information deteriorates
Solution Approach 1:
The system performs preliminary compression of the precoding matrix indicator (PMI) at the user equipment before transmission. By compressing the PMI data in advance using codebook-based representations and selective parameter transmission, the uplink overhead is reduced while the essential precoding information is preserved for effective beamforming at the base station.
Solution Approach 2:
The invention changes the representation parameters of precoding information by transitioning from transmitting complete precoding matrices to transmitting compressed PMI indicators that reference codebook entries. This parameter transformation reduces the data volume significantly while maintaining the ability to reconstruct effective precoding information at the receiver.
2Reliability
If iterative operations like gradient descent are used to optimize precoding information, then the correlation and effectiveness improve, but the computational complexity increases
Solution Approach 1:
Instead of performing exhaustive iterative optimization at both transmitter and receiver, the system applies partial optimization by using codebook-based PMI selection and gradient descent only for determining scaling coefficients. This partial application of iterative methods achieves sufficient correlation improvement while avoiding the excessive computational burden of full matrix optimization.
Solution Approach 2:
The invention introduces an intermediary codebook structure that bridges the transmitter and receiver. The codebook serves as a pre-computed reference that eliminates the need for real-time iterative optimization at both ends, reducing computational complexity while maintaining reliable precoding through the intermediary lookup and scaling coefficient adjustment.
Data Source
AI summary
A method may include determining, for a channel, a target component of precoding information, determining at least a part of the precoding information based on the target component and a decompressed component of the precoding information, and sending, from a user equipment, the at least a part of the precoding information. Determining the at least a part of the precoding information may be based on a correlation between the target component and the decompressed component. The at least a part of the precoding information may include a vector of coefficients. The vector of coefficients may include a vector of complex numbers. At least one of the complex numbers may include an amplitude representing a scaling coefficient and a phase representing a rotation coefficient. The target component may include a target matrix. The decompressed component may include a decompressed matrix. The target matrix may include a linear combination coefficients matrix. The precoding information may include a precoding matrix indicator.