QAM Blind Equalization Using Euclidean-Distance Cost Functions
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Solution Overview
Problem
Existing blind equalization algorithms for QAM signals, such as the Constant Modulus Algorithm (CMA) and its modifications, are not optimal as they produce high mean square error and are insensitive to the carrier phase, requiring complex carrier phase estimation and consuming significant computing resources.
Innovation Solution
A Square Modulus Algorithm (SMA) is introduced that calculates a cost function based on the minimum Euclidean distance between constellation points, enabling high-performance blind equalization of QAM signals without the need for separate carrier phase estimation by dynamically adjusting tap weights using a SMA/DD-LMS equalizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If blind equalization algorithms such as CMA are used for QAM signals, then equalization can be performed without training sequences, but the mean square error is high and the algorithm is insensitive to carrier phase
Solution Approach 1:
The patent changes the cost function parameter from constant modulus (CMA) to squared Euclidean distance (SMA). This parameter change makes the algorithm sensitive to carrier phase while maintaining blind equalization capability, and significantly reduces mean square error for QAM signals.
2Ease of operation
If CMA-based blind equalization is used, then carrier phase estimation is required separately, but this increases computational complexity and resource consumption
Solution Approach 1:
The patent merges the blind equalization function and carrier phase estimation function into a single SMA algorithm. The squared Euclidean distance cost function simultaneously performs both equalization and phase estimation, eliminating the need for separate carrier phase estimation and reducing computational complexity.
3Measurement precision
If separate carrier phase estimation is implemented after CMA equalization, then phase recovery can be achieved, but computing resources are significantly consumed
Solution Approach 1:
The patent combines carrier phase recovery into the blind equalization process itself through the SMA cost function. This integration achieves accurate phase recovery while minimizing computing resource consumption by performing both functions in a single algorithmic pass rather than sequentially.
Data Source
AI summary
A system and method for blind equalization of a QAM signal. Equalization is achieved using an algorithm characterized by cost function that is a function the Euclidian distance, e.g. the minimum Euclidian distance, between points of the constellation associated with the QAM signal, i.e. the distance between symbols.