Frequency-Domain Signal Compression for Low-Power User SNR
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Solution Overview
Problem
Current data compression methods in wireless communication systems, particularly using the CPRI protocol, result in significant signal noise ratio loss for low power users due to lossy compression, which affects the overall data compression ratio, mean square error (MSE), and error vector magnitude (EVM).
Innovation Solution
The method involves converting data from the time domain to the frequency domain, identifying weak power frequencies, weighting them, converting back to the time domain, compressing, and transmitting with weighting information, using either a rough or fine identification rule, to minimize lossy compression effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression is applied to transmitted data in the time domain or frequency domain, then data compression ratio is improved, but signal noise ratio loss increases for low power users
Solution Approach 1:
The patent applies different compression strategies to different frequency components based on their power characteristics. Strong power frequencies are compressed with higher aggressiveness while weak power frequencies are preserved with higher quality, achieving local optimization of compression quality across the frequency spectrum.
Solution Approach 2:
The patent performs preliminary identification and classification of frequency components into strong and weak power categories before applying compression. This preliminary action allows the system to prepare appropriate compression parameters for each frequency type, preventing excessive noise introduction to weak signals.
2Reliability
If weak power frequencies are identified and weighted, then signal noise ratio loss is reduced for low power users, but device complexity increases
Solution Approach 1:
The patent segments the frequency spectrum into distinct strong power and weak power frequency components. This segmentation allows independent processing of each segment with appropriate compression parameters, reducing the need for complex global optimization algorithms.
Solution Approach 2:
The patent changes compression parameters dynamically based on frequency power characteristics. By adjusting compression strength according to the identified power levels of different frequencies, the system achieves adaptive compression without requiring complex real-time optimization algorithms.
3Productivity
If data is converted to frequency domain for compression, then compression efficiency is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential weighting information about weak power frequencies rather than processing and transmitting all frequency domain data. This extraction approach maintains compression efficiency while reducing the processing burden of frequency domain transformations.
Data Source
AI summary
A method, device, and system for data compression and decompression are provided. The method for data compression comprises, converting data to be transmitted within each period, from the time domain to the frequency domain, wherein, a default time length is set as a period; identifying weak power frequencies in the frequency domain data according to a set identification rule; weighting data transmitted on the identified weak power frequencies to obtain corresponding weighting information; converting other data converted to the frequency domain and the weighted data back to time domain; compressing the data converted back to the time domain; and transmitting, the compressed data along the weighting information.