AI Signal Compression for High-Fidelity IQ Data Storage
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Solution Overview
Problem
Current methods for compressing IQ data, such as μ-law compression, result in reduced data fidelity and unsatisfactory compression ratios, especially with increasing bandwidths and longer record lengths in RF applications, necessitating a more effective method to store larger amounts of IQ data without significant loss of information.
Innovation Solution
A method utilizing an artificial intelligence module to transform digital signal data into a suitable domain, detect and classify wanted signal portions, and store only the relevant data, discarding irrelevant portions like noise, while using meta-information for reconstruction, thereby reducing memory needs without information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If μ-law compression is used to compress IQ data, then the data storage capacity increases, but the data fidelity deteriorates
Solution Approach 1:
The patent segments the signal into wanted signal portions and unwanted signal portions (such as noise and interference). By identifying and separately handling these segments, the system can compress only the relevant wanted signal portions while maintaining their fidelity, thus resolving the contradiction between compression ratio and data quality
Solution Approach 2:
The patent transforms the signal from the time domain to the frequency domain using Fourier transform, changing the representation parameters of the signal. This transformation enables better identification and separation of wanted and unwanted signal portions, allowing for selective compression that maintains fidelity of important components while achieving compression
2Quantity of substance
If compression ratio is increased to handle larger IQ data volumes, then the storage requirements decrease, but the information loss increases
Solution Approach 1:
The patent extracts and identifies wanted signal portions from the mixed signal using signal detection and classification algorithms. By taking out only the relevant wanted signal portions for storage and discarding unwanted portions (noise, interference), the system achieves compression without losing important information, thus resolving the contradiction between storage efficiency and information preservation
Solution Approach 2:
The patent discards unwanted signal portions (noise and interference) that do not contain useful information, while recovering and preserving the wanted signal portions. This selective discarding and recovering approach enables compression ratio improvement without significant information loss of the actual signal content
Data Source
AI summary
A method of compressing digital signal data obtained from a signal is described. The method includes: receiving digital signal data associated with a signal and/or generating digital signal data based on a signal; transforming the digital signal data into a transform domain, thereby generating transformed digital signal data; determining at least one characteristic parameter based on the transformed digital signal data by an artificial intelligence circuit; detecting and/or classifying at least one wanted signal portion based on the at least one characteristic parameter by the artificial intelligence circuit; and storing only a subset of the digital signal data that is associated with the at least one wanted signal portion. Further, a signal compressor circuit for compressing digital signal data obtained from a signal and a computer program are described.