Continuous-Time FFE-DFE Tap Fixing for Stable Blind LMS Convergence
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Solution Overview
Problem
Existing adaptive equalization systems require training and are not capable of continuous time adaptation, especially when dealing with multidimensional codes and interdependent symbols, leading to issues like coefficient drift and sensitivity to timing mismatches.
Innovation Solution
A digital signal processing system using a combination of continuous time feed forward equalization (FFE) and decision feedback equalization (DFE) with specific tap coefficients fixed to a unique voltage, ensuring stability and convergence to a minimum mean square error (MMSE) solution, even in the presence of timing mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training is used for coefficient adaptation, then initial convergence is achieved, but the system cannot adapt continuously and requires periodic retraining
Solution Approach 1:
The system performs self-adaptation through continuous blind LMS algorithm operation without requiring external training sequences. The equalizer coefficients are continuously updated using the error signal between the equalized output and the sliced output, enabling the system to adapt to channel changes autonomously and continuously without stopping operation for retraining
Solution Approach 2:
The coefficient adaptation process operates continuously in the blind mode rather than periodically during training phases. The LMS algorithm continuously updates coefficients using the available error signal from the slicer, ensuring uninterrupted adaptation capability and eliminating downtime associated with periodic retraining operations
2Adaptability or versatility
If all taps are allowed to drift freely, then the system can adapt to channel changes, but coefficient instability and drift occur
Solution Approach 1:
Different taps are assigned different characteristics: selected reference taps are held fixed at specific voltage levels to provide stable reference points, while other taps are allowed to drift freely to adapt to channel changes. This local differentiation in tap behavior provides both stability and adaptability simultaneously
Solution Approach 2:
The system changes the voltage parameter of selected taps from variable to fixed values to create stable reference points. By fixing specific taps at predetermined voltage levels while leaving others variable, the system establishes a stable foundation for the adaptation process while maintaining overall channel adaptation capability
3Productivity
If the system uses blind adaptation without training, then continuous operation is maintained, but convergence robustness and stability are reduced
Solution Approach 1:
Selected taps are pre-set to specific voltage levels before the adaptation process begins. These predetermined reference taps provide a stable starting point and reference framework that guides the blind adaptation process, improving convergence robustness while maintaining continuous operation
Solution Approach 2:
The system uses feedback from the slicer error signal to continuously update the LMS coefficients. The error signal provides continuous information about the deviation from the desired response, enabling robust convergence even in blind mode. The feedback mechanism ensures that the adaptation process remains guided and stable without requiring training sequences
Data Source
AI summary
A continuous time electronic dispersion compensation architecture using feed forward equalization and a non-linear decision feedback equalization forms an output signal by a linear combination of successively delayed versions of the input signal and the sliced output signal weighted by appropriate coefficients. A selected number of taps in the mixer used to generate a corresponding number of coefficients for use in the feed forward equalizer are held to a selected voltage to ensure that the coefficients associated with these two taps do not drift. This causes the other coefficients to converge to a unique minimum square error value. In one embodiment the selected voltage is the maximum system voltage.