Integer Signal Spectrum Sampling for Lossless Bandwidth Reduction
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Solution Overview
Problem
Existing compression methods either use lossy compression techniques that alter the original signal or require large dictionaries for lossless compression, necessitating improved performance and reduced hardware resources.
Innovation Solution
The method involves performing a Fast Fourier Transform on an integer signal, sampling its frequency spectrum, and transmitting only the necessary samples, allowing for lossless compression and efficient recovery of the original signal without the need for a large dictionary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If lossy compression is used to remove insensitive frequency bands, then transmission bandwidth is reduced, but signal fidelity deteriorates
Solution Approach 1:
The patent extracts and transmits only the essential frequency components of the signal. By performing FFT and selecting only certain frequency bins (e.g., DC component and fundamental frequencies) for transmission, the method transmits a minimal subset of frequency data that suffices for accurate integer signal recovery, thereby reducing bandwidth while maintaining fidelity.
Solution Approach 2:
The patent changes the representation parameters of the signal from time-domain samples to selective frequency-domain components. By transforming to frequency domain via FFT and transmitting only specific frequency parameters rather than all time-domain samples, the method achieves compression while preserving the ability to exactly reconstruct integer signals.
2Reliability
If a large dictionary is used for lossless compression, then signal fidelity is maintained, but storage space and device complexity increase
Solution Approach 1:
Instead of using a large pre-stored dictionary, the patent extracts the essential frequency components directly from the input signal through FFT. This eliminates the need for extensive pre-stored reference data while maintaining lossless compression capability, as the extracted frequency parameters are transmitted and used for exact signal recovery.
Solution Approach 2:
The patent performs FFT transformation and frequency component selection in advance before transmission. By pre-processing the signal to identify and extract only the necessary frequency components that will be needed for reconstruction, the method avoids the need for large dictionaries at either transmitter or receiver, reducing storage requirements while enabling lossless compression.
3Loss of energy
If frequency spectrum sampling is performed to reduce transmission data, then bandwidth usage is reduced, but signal recovery accuracy may deteriorate
Solution Approach 1:
The patent changes from transmitting time-domain samples to transmitting selected frequency-domain parameters. By transforming to the frequency domain and selecting specific frequency bins based on signal characteristics, the method identifies the minimal set of parameters needed for exact integer signal recovery, maintaining precision while reducing transmission data.
Solution Approach 2:
The recovery process uses the transmitted frequency parameters to reconstruct the signal, and the integer constraint provides implicit feedback for verification. The system checks whether the recovered signal consists of valid integers, ensuring recovery accuracy without requiring transmission of additional verification data.
Data Source
AI summary
The present disclosure provides a technology for sampling frequency spectrums of integer signals and recovering integer signals. For a known signal sequence consisting of integers, it is sufficient to select proper samples from a Fourier transform frequency spectrum of the signal sequence and transmit the selected samples, thereby achieving the effect of signal compression. Further, the receiver apparatus can recover the integer signal sequence without any distortion by the method disclosed in the present disclosure.