Cyclic Autocorrelation Co-Channel Interference Detection
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Solution Overview
Problem
Conventional methods for detecting co-channel interference in wireless communications are inefficient, often requiring extensive resource usage and prone to false alarms, with existing techniques struggling to accurately identify interference in real-time due to their reliance on sweeping frequency analysis or energy-based detection methods.
Innovation Solution
The implementation of a multi-branch cyclic autocorrelation test to determine the presence of interfering signals by calculating total correlation metrics from individual branches, allowing for reduced detection time and improved accuracy with a target detection probability and false alarm likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sweeping frequency analysis or energy-based detection methods are used, then interference detection can be performed, but detection time is excessive and false alarm rate is high
Solution Approach 1:
The patent changes the detection parameter from general energy-based detection to cyclic autocorrelation-based detection, which exploits the cyclic properties of digital communication signals. This parameter change enables more accurate interference detection with reduced detection time, as the cyclic autocorrelation function can identify interference signals more efficiently than conventional methods
Solution Approach 2:
The patent segments the detection process into multiple branches, each handling specific cyclic frequency components. By dividing the detection task into parallel branches that process different cyclic properties independently, the system achieves faster detection while maintaining high accuracy through combined results from all branches
2Measurement precision
If conventional sweeping frequency analysis is used, then interference detection can be performed, but resource usage is excessive
Solution Approach 1:
The patent transitions from resource-intensive sweeping frequency analysis to efficient cyclic autocorrelation detection. This parameter change reduces computational resources and energy consumption while maintaining or improving detection accuracy, as cyclic autocorrelation directly targets the characteristic properties of digital communication signals without requiring broad frequency sweeping
Solution Approach 2:
By segmenting the detection into parallel branches that can be processed independently, the patent enables more efficient resource utilization. Each branch processes specific cyclic frequency components separately, allowing for optimized resource allocation and reduced overall computational burden compared to conventional unified detection approaches
3Productivity
If conventional energy-based detection methods are used, then interference detection can be performed, but false alarm likelihood is high
Solution Approach 1:
The patent changes the detection parameter from general signal energy to cyclic autocorrelation properties, which are characteristic of digital communication signals. This parameter change significantly reduces false alarm rates because cyclic autocorrelation specifically identifies the periodic structure of digital signals, making it more reliable than energy-based detection that can trigger false alarms from any signal energy
Solution Approach 2:
The multi-branch structure segments the detection to analyze different cyclic frequency components separately. This segmentation allows the system to cross-validate detection results across multiple branches, reducing false alarms by requiring consistent detection across different cyclic properties before confirming interference presence
Data Source
AI summary
Technologies directed to co-channel interference detection using a signal correlation test are described. One method includes receiving a radio frequency (RF) signal and generating digital sample corresponding to the RF signal. The method further includes determining a first correlation coefficient using the digital samples, a first value, and a second value. The first value corresponds to a signal lag parameter and the second value corresponds to a cyclic frequency parameter. The method further includes determining a second correlation coefficient using the digital samples, the first value, and a third value corresponding to the cyclic frequency parameter. The second value and the third value are multiples of a fourth value. The method further includes determining, using the first correlation coefficient and the second correlation coefficient, that first RF signal comprises a first portion from a second communication device and a second portion from a third communication device.