Adaptive Equalizer Using Feed-Forward Feedback Carrier Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Optical communication systems face challenges in quickly adapting to signal distortion and inter-symbol interference, particularly due to polarization transients caused by lightning strikes, which existing equalizers struggle to mitigate effectively.
Innovation Solution
The implementation of an adaptive equalizer architecture that uses both feed-forward and feedback carrier recovery, combined with LMS tap updating, to quickly adjust filter weights and compensate for rapidly changing channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional equalizers are used to mitigate signal distortion and ISI, then they can compensate for non-ideal channels, but they cannot adapt quickly enough to rapidly varying channel conditions caused by polarization transients
Solution Approach 1:
The equalizer transitions from a static filter structure to a dynamic adaptive system using LMS algorithm. The filter coefficients are continuously updated based on the least-mean-square error criterion, allowing the equalizer to dynamically track and adapt to rapidly varying channel conditions caused by polarization transients, thereby maintaining reliability during fast channel changes
Solution Approach 2:
The LMS adaptive equalizer implements feedback mechanisms where the equalized output is compared with the desired signal to generate an error signal. This error signal is fed back to continuously adjust the filter coefficients, creating a closed-loop system that automatically adapts to channel variations and mitigates polarization transients effectively
2Adaptability or versatility
If filter coefficients are updated using conventional LMS adaptation, then the equalizer can adapt to channel changes, but the adaptation process introduces delay that reduces productivity
Solution Approach 1:
The system performs preliminary actions by maintaining a tapped delay line structure that pre-computes and stores delayed signal samples. This preliminary preparation of signal history allows the LMS algorithm to quickly compute coefficient updates without introducing significant processing delay, thereby maintaining high data processing speed while adapting to channel changes
3Reliability
If tapped delay-line filters are used to compensate for channel effects, then signal distortion and ISI can be mitigated, but the device complexity increases when multiple filters and feedback paths are added
Solution Approach 1:
The equalizer is segmented into distinct functional modules: a tapped delay-line filter for signal processing, an LMS adaptation module for coefficient updates, and optional feedback carrier recovery blocks. This modular segmentation allows each component to perform its specific function efficiently while maintaining overall system reliability, and enables independent optimization of each module without increasing overall complexity excessively
Data Source
AI summary
Apparatus and methods may provide improved equalizer performance, e.g., for optical-fiber-based communication systems. A least-mean-square (LMS) equalizer may include a decision feedback path containing feedback carrier recovery (FBCR), which may have low latency, and which may thus enable high-speed tap updating in the equalizer. Feed-forward carrier recovery (FFCR) may be applied, in parallel with the FBCR, to provide equalizer output by compensating, e.g., for phase noise, with improved carrier recovery/compensation, versus using FBCR to generate the output.