Tone Interference Estimation via Segmented DFT Processing
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Solution Overview
Problem
Current communication systems face limitations in reliably estimating tone interference frequency and amplitude due to high computational complexity and performance constraints, particularly in 4G cellular networks, where existing methods are limited by the size of the discrete Fourier transform (DFT) and require significant processing delay.
Innovation Solution
The system employs an N-point transform module to convert time-domain data to frequency-domain data and a K-point transform module to generate K-point data, coupled with a frequency calculation module to determine tone interference frequency, allowing for improved estimation and reduced computational complexity by processing multiple DFT blocks and using a two-dimensional interpolator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large-size DFT is used to improve tone interference frequency estimation accuracy, then measurement precision is improved, but device complexity and processing delay increase
Solution Approach 1:
The patent segments the frequency estimation process into two stages: first performing a coarse estimation using a smaller N-point DFT to identify candidate frequency bins, then performing fine estimation only on those candidates using a K-point DFT. This segmentation avoids the need for a single large-size DFT while achieving comparable or better estimation accuracy, thereby reducing computational complexity and processing delay.
Solution Approach 2:
The patent performs preliminary coarse frequency estimation using an N-point DFT before conducting the final fine estimation. This preliminary action narrows down the search space to only those frequency bins that are likely to contain tone interference, allowing the subsequent K-point DFT to focus computational resources efficiently and achieve high precision without requiring a large transform size from the outset.
2Measurement precision
If a large-size DFT is used to improve tone interference frequency estimation accuracy, then measurement precision is improved, but processing delay increases
Solution Approach 1:
The patent segments the frequency estimation process into two stages: first performing a coarse estimation using a smaller N-point DFT to identify candidate frequency bins, then performing fine estimation only on those candidates using a K-point DFT. This segmentation avoids the need for a single large-size DFT while achieving comparable or better estimation accuracy, thereby reducing computational complexity and processing delay.
Solution Approach 2:
The patent performs preliminary coarse frequency estimation using an N-point DFT before conducting the final fine estimation. This preliminary action narrows down the search space to only those frequency bins that are likely to contain tone interference, allowing the subsequent K-point DFT to focus computational resources efficiently and achieve high precision without requiring a large transform size from the outset.
3Device complexity
If existing DFT-based methods are used for tone estimation, then device complexity is reduced, but measurement precision deteriorates due to DFT size limitations
Solution Approach 1:
The patent segments the frequency estimation process into two stages: first performing a coarse estimation using a smaller N-point DFT to identify candidate frequency bins, then performing fine estimation only on those candidates using a K-point DFT. This segmentation avoids the need for a single large-size DFT while achieving comparable or better estimation accuracy, thereby reducing computational complexity and processing delay.
Solution Approach 2:
The patent performs preliminary coarse frequency estimation using an N-point DFT before conducting the final fine estimation. This preliminary action narrows down the search space to only those frequency bins that are likely to contain tone interference, allowing the subsequent K-point DFT to focus computational resources efficiently and achieve high precision without requiring a large transform size from the outset.
Data Source
AI summary
A method of operation of a communication system includes: converting time-domain data to frequency-domain data based on an N-point transform size; generating K-point data based on the frequency-domain data and the N-point transform size; and determining a tone interference frequency based on the K-point data for elimination of tone interference to improve system performance.