Modulation Recognition via Constellation Distance Statistics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing modulation recognition algorithms in communication systems, such as pattern recognition and maximum likelihood recognition, face challenges in noisy environments with high complexity and low convergence rates, particularly in correctly identifying modulation types without prior information.
Innovation Solution
A method and apparatus that de-map an input signal to find the nearest points in constellations corresponding to different modulation types, calculate distance statistics values, and compare these values to quickly and accurately recognize the modulation type, unaffected by carrier and phase offset, with low complexity and high convergence rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern recognition is used for modulation recognition, then high-order statistics data or signal envelop characteristics can be obtained, but the implementation becomes complicated and performance decreases in noisy environments
Solution Approach 1:
The patent extracts only the essential feature (signal magnitude or power) needed for modulation recognition, eliminating the need for complex high-order statistics calculations. By focusing on the magnitude of constellation points rather than full complex signal processing, the method achieves accurate modulation type identification with significantly reduced computational complexity
Solution Approach 2:
The patent uses simple magnitude comparison operations instead of complex pattern recognition algorithms. The method employs basic arithmetic operations (absolute value, squaring, comparison) that are computationally inexpensive and can be implemented with simple hardware, making the recognition process both fast and resource-efficient
2Measurement precision
If maximum likelihood recognition is used, then recognition can be performed under maximum likelihood premise, but low convergence rate requires large amount of monitoring samples in noisy environment
Solution Approach 1:
The patent uses a simplified distance comparison metric that provides sufficient discrimination between modulation types without requiring exhaustive statistical analysis. By using partial information (magnitude only) rather than complete signal characteristics, the method achieves fast convergence with minimal samples while maintaining adequate recognition accuracy
Solution Approach 2:
The patent transforms the recognition problem from comparing complex signal parameters to comparing simple magnitude parameters. This parameter transformation (from complex signal to magnitude/power) simplifies the convergence criteria and enables faster recognition with fewer samples, particularly in noisy environments where full signal analysis would be time-consuming
Data Source
AI summary
A method and an apparatus for modulation recognition in communication system are provided. First, a plurality of constellation corresponding different modulation types are provided, wherein each constellation has a plurality of points. An input signal is de-mapped to find out a nearest point located nearest to a position of the input signal in each constellation. The distances from the nearest points to the position of the input signal are respectively counted to obtain a plurality of distance statistics values corresponding to different modulation types. The apparatus compares the distance statistics values for recognizing the modulation type of the input signal.