Adaptive Interference Mitigation in Wide Band Spectrum
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Solution Overview
Problem
Conventional adaptive interference mitigation techniques in wide-band communication systems are inefficient in reducing spectral leakage and require significant hardware resources, particularly in portable devices where memory and processing power are limited.
Innovation Solution
The proposed solution involves a filter device that performs adaptive interference mitigation by doubling the input sample size for FFT processing, applying a windowing function, and calculating noise floors using log-scale average magnitude values to identify and reduce interfering signals, thereby reducing spectral leakage and hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional adaptive interference mitigation techniques are used, then interference can be reduced, but spectral leakage is not effectively reduced and hardware resources are significantly consumed
Solution Approach 1:
The received signal block is divided into multiple sub-blocks for processing. Each sub-block is processed independently through FFT, interference identification, and excision operations, then combined to form the final filtered signal. This segmentation allows the system to handle wideband signals with multiple interferers more efficiently, reducing the computational burden on hardware resources while maintaining effective interference mitigation.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the signal spectrum. The excision operation selectively removes only the frequency bins containing interferers rather than processing the entire spectrum uniformly. This approach reduces hardware resource consumption by focusing computational effort only where interference is present, while still achieving effective interference mitigation.
2Device complexity
If FFT processing is performed on standard input sample sizes, then processing is simpler, but spectral leakage increases and interference mitigation effectiveness decreases
Solution Approach 1:
The signal processing is segmented into multiple stages: the received block is divided into sub-blocks, each processed through FFT with appropriate windowing. This segmentation approach effectively reduces spectral leakage by ensuring that each FFT operation works with properly windowed segments, while the overall processing remains systematic and manageable through the structured multi-stage approach.
Solution Approach 2:
Windowing functions are applied to each sub-block before FFT processing to pre-condition the signal and minimize spectral leakage effects. This preliminary action ensures that when the FFT is performed, the spectral leakage is already reduced, improving interference mitigation effectiveness without complicating the subsequent processing steps.
3Object-affected harmful factors
If larger FFT block sizes are used to reduce spectral leakage, then interference mitigation improves, but memory requirements and processing complexity increase
Solution Approach 1:
Instead of using a single large FFT block that would consume excessive memory, the patent segments the received signal into smaller sub-blocks. Each sub-block is processed through FFT independently, reducing the memory requirements for each FFT operation. The segmented approach maintains effective spectral leakage reduction while keeping memory usage manageable by processing smaller portions of the signal at each stage.
Solution Approach 2:
The patent applies partial action by processing only the necessary sub-blocks of the received signal rather than the entire block at once. This selective processing reduces memory requirements while still achieving effective interference mitigation across the full bandwidth by combining the results from multiple sub-block processing operations.
Data Source
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AI summary
A filter for a radio receiving device doubles a sample size of a digital baseband signal to form an enhanced data set and after widowing performs a Fast Fourier Transform (FFT) to facilitate calculation of instantaneous magnitude values and average magnitude values for each of a plurality of frequency bins. A noise floor is calculated based on the average magnitude values of a plurality of the frequency bins, and the noise floor is then used to identify frequency bins which contain an interfering signal. If it is determined that a frequency bin contains spectral energy associated with an interfering signal, then the instantaneous magnitude value of the frequency bin is selectively reduced, after which an inverse FFT operation and inverse window operation is performed.