IQ Data Compression Using Significant Transform Coefficients
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for compressing IQ measurement data, such as μ-law compression, result in significant data fidelity loss and unsatisfactory compression ratios, especially with increasing bandwidths and longer record lengths in RF applications.
Innovation Solution
The method involves transforming IQ data into a suitable domain (time, frequency, or time-frequency) to exploit redundancy, assessing transform coefficients for significance, and storing only significant coefficients along with indices, allowing for improved compression by retaining only essential data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If μ-law compression is used to compress IQ measurement data, then the data storage capacity is reduced, but the data fidelity is significantly reduced
Solution Approach 1:
The patent extracts only the significant transform coefficients from the IQ data after transformation, storing only these important components along with their indices. This selective extraction approach reduces storage requirements while maintaining data fidelity by preserving only the essential information needed for accurate signal reconstruction.
Solution Approach 2:
The patent applies a transformation to change the parameter representation of IQ data from the time domain to a transform domain (such as frequency domain). This parameter change enables redundancy exploitation and allows selective retention of significant coefficients, achieving better compression ratios without significant fidelity loss.
2Quantity of substance
If compression ratio is increased to handle larger IQ data volumes, then the storage requirement is reduced, but the error vector magnitude increases
Solution Approach 1:
The patent extracts only the significant transform coefficients from the IQ data after transformation, storing only these important components along with their indices. This selective extraction approach reduces storage requirements while maintaining data fidelity by preserving only the essential information needed for accurate signal reconstruction.
Solution Approach 2:
The patent applies partial action by retaining only a subset of transform coefficients that are deemed significant based on predefined criteria. This partial retention strategy achieves high compression ratios while maintaining acceptable fidelity by keeping only the most important data components for signal reconstruction.
3Loss of information
If all IQ data is retained to maintain signal fidelity, then the data accuracy is preserved, but the storage capacity requirement increases
Solution Approach 1:
The patent extracts only the significant transform coefficients from the IQ data after transformation, storing only these important components along with their indices. This selective extraction approach reduces storage requirements while maintaining data fidelity by preserving only the essential information needed for accurate signal reconstruction.
Solution Approach 2:
The patent applies a transformation to change the parameter representation of IQ data from the time domain to a transform domain (such as frequency domain). This parameter change enables redundancy exploitation and allows selective retention of significant coefficients, achieving better compression ratios without significant fidelity loss.
Data Source
AI summary
A method for compressing IQ measurement data obtained from a signal is described. Within the IQ measurement data, at least one block of IQ data is determined where redundancy of the respective data can be exploited. The IQ data of the at least one block is transformed into a transform domain where redundancy of the respective data can be exploited. Transform coefficients obtained in the transform domain are determined and assessed with regard to a pre-defined criteria so as to determine whether the transform coefficients comprise significant information. Only the IQ data assigned to transform coefficients having significant information is stored along with indices in the transform domain indicating where the respective transform coefficients occur in the transform domain.