Iterative Signal Detection in Shared Frequency Bands
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Solution Overview
Problem
In environments where multiple radio communication systems share the same frequency band, existing signal detection methods struggle to accurately identify and differentiate between various signals, leading to inefficient spectrum use and interference issues, particularly when weak signals are masked by stronger ones.
Innovation Solution
A signal detection apparatus and method that calculates waveform feature amounts using second-order cyclic autocorrelation functions and test statistics, allowing for the identification of detection target signals by comparing these statistics with thresholds, and iteratively recalculates test statistics after removing detected signals to enhance the detection of weaker signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If feature detection is used to identify signals with small amount of advance information, then adaptability is improved, but detection precision deteriorates when weak signals are masked by strong signals
Solution Approach 1:
The patent segments the signal detection process into multiple stages: initial feature detection to identify potential signals, followed by iterative interference cancellation to separate weak signals from strong ones. This segmentation allows the system to first adapt to various signal types using feature detection, then achieve precise detection of weak signals through subsequent processing stages.
Solution Approach 2:
The patent extracts and removes the influence of strong signals from the received signal before detecting weak signals. By iteratively identifying and subtracting detected signals from the composite signal, the system isolates weak signals that would otherwise be masked, thereby improving detection precision while maintaining adaptability.
2Productivity
If multiple radio systems share the same frequency band, then frequency utilization is improved, but interference increases
Solution Approach 1:
The patent implements feedback through iterative detection and interference cancellation. The system detects signals, removes their contribution from the received signal, and repeats the process to detect remaining signals. This feedback loop continuously refines the detection accuracy and reduces interference effects, enabling multiple systems to coexist in the same frequency band.
Solution Approach 2:
The patent changes detection parameters dynamically during the iterative process. As strong signals are removed, the system adjusts its detection threshold and parameters to optimize for weaker signals that become more prominent in the residual signal, thereby managing interference while maintaining high frequency utilization.
3Productivity
If signal detection is performed in shared frequency bands, then spectrum efficiency is improved, but detection accuracy deteriorates due to signal masking
Solution Approach 1:
The patent performs preliminary detection of strong signals before attempting to detect weak signals. By first identifying and removing the dominant signal components, the system prepares the signal environment for subsequent detection of weaker signals, thereby maintaining both spectrum efficiency and detection accuracy.
Solution Approach 2:
The patent introduces an intermediary processing stage between receiving the composite signal and final detection. This intermediary stage performs iterative interference cancellation, acting as a mediator that separates overlapping signals and preserves both the efficiency gains from spectrum sharing and the accuracy needed for reliable detection.
Data Source
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AI summary
A signal detection apparatus 23 for determining whether a detection target signal is included in a received radio signal, includes: a waveform feature amount calculation unit 31 configured to calculate a waveform feature amount Rxα representing a waveform feature; a test statistic calculation unit 32 configured to calculate test statistic Zxα of each detection target signal by using the waveform feature amount; and a signal decision unit 33 configured to determine presence or absence of each detection target signal by comparing the test statistic Zxα of each detection target signal with a threshold r, wherein, under a condition where a specific detection target signal is removed, the test statistic calculation unit calculates a test statistic for a detection target signal which is not removed, and the signal decision unit determines presence or absence of the detection target signal by comparing the calculated test statistic with the threshold.