Wavelet-Transformed Channel Information Compression in 5G Terminals
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Solution Overview
Problem
Current channel information transfer methods in communication systems, particularly in 5G and beyond, face challenges in efficiently handling channel characteristics changes due to the discontinuity of eigenvectors with surrounding data, leading to performance degradation in intelligent compression and decompression techniques.
Innovation Solution
The proposed method employs wavelet transform to preprocess and post-process channel information, using a neural network model that includes wavelet transformers and encoders to decompose and recompress channel information across various frequency bands, thereby preserving high-frequency components and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intelligent compression and decompression techniques are used for channel information transfer, then data transmission efficiency is improved, but performance degrades due to discontinuity of eigenvectors with surrounding data
Solution Approach 1:
The patent segments channel information into different frequency bands using wavelet transform, separating high-frequency and low-frequency components. This segmentation allows different processing strategies for different frequency components, improving both compression efficiency and reconstruction accuracy by addressing the discontinuity issue in a structured manner.
Solution Approach 2:
The patent introduces wavelet transform as an intermediary processing step between the original channel information and the compression algorithm. This intermediary transformation converts the discontinuous eigenvector data into a continuous wavelet domain representation, enabling more effective compression while preserving essential channel characteristics.
2Stability of the object's composition
If wavelet transform is applied to channel information, then continuity with surrounding data is improved, but processing complexity increases
Solution Approach 1:
The wavelet transform process is segmented into distinct stages: decomposition into different frequency bands, selective processing of high-frequency components, and reconstruction. This segmentation manages complexity by breaking down the transform into manageable steps rather than applying a monolithic complex algorithm.
Solution Approach 2:
The patent applies different processing qualities to different frequency bands. High-frequency components receive enhanced processing to preserve discontinuous features, while low-frequency components are processed with standard algorithms. This local quality approach improves overall continuity without uniformly increasing processing complexity across all data.
Data Source
AI summary
A method of a terminal may comprise: receiving a reference signal from a base station; generating channel information based on the reference signal; generating wavelet-transformed channel information by applying wavelet transform to the channel information; generating compressed channel information by compressing the wavelet-transformed channel information; and transmitting the compressed channel information to the base station.