Adaptive Equalizer Loop Gain for Optical Channel Tracking
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Solution Overview
Problem
Blind adaptive channel equalizers face challenges in converging quickly and tracking channel variations, especially in the presence of probabilistic shaped constellations and high equalizer loop delays, which degrade performance and lead to self-oscillations in optical communication systems.
Innovation Solution
Applying different loop gains to specific frequency components of the adaptive filter feedback using bandpass filters to isolate and amplify frequency bands with more channel variation, thereby improving convergence speed and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block processing is performed in the channel equalizer loop, then data throughput is improved, but processing delay increases proportionally to block size
Solution Approach 1:
The patent segments the equalizer loop processing into parallel blocks that can be processed simultaneously. By dividing the received signal into multiple blocks and processing them in parallel, the system achieves higher throughput while keeping individual block delays manageable. The segmented approach allows the equalizer to process multiple blocks concurrently rather than sequentially.
2Productivity
If loop delay in the channel equalizer increases, then block processing capability is improved, but convergence speed of the equalizer decreases
Solution Approach 1:
The patent implements dynamic loop gain adjustment that adapts based on the equalizer's convergence state and current block processing requirements. The loop gain is increased during initial convergence phases to accelerate adaptation, then reduced during steady-state operation to maintain stability with larger block sizes. This dynamic adjustment allows the system to achieve fast convergence despite increased loop delays from block processing.
3Device complexity
If blind equalization is used to avoid training stage, then system complexity is reduced, but convergence performance deteriorates with shaped constellations
Solution Approach 1:
The patent modifies the equalization algorithm parameters specifically for shaped constellation detection. It adjusts the error constraint function parameters and loop gain scheduling to account for the non-uniform probability distribution of shaped constellations. By changing these parameters, the blind equalizer can converge effectively on shaped constellations without requiring a training stage, maintaining low system complexity while improving convergence reliability.
4Speed
If loop gain is increased to speed up convergence, then convergence speed is improved, but self-oscillations increase in time-varying channels
Solution Approach 1:
The patent implements dynamic loop gain adjustment that adapts based on the equalizer's convergence state and current block processing requirements. The loop gain is increased during initial convergence phases to accelerate adaptation, then reduced during steady-state operation to maintain stability with larger block sizes. This dynamic adjustment allows the system to achieve fast convergence despite increased loop delays from block processing.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor the equalizer's output for signs of self-oscillation. When oscillations are detected, the system automatically reduces the loop gain to dampen the oscillations. This feedback control allows the system to maintain high convergence speed when stable, while automatically preventing self-oscillations when they occur, effectively resolving the trade-off between speed and stability.
Data Source
AI summary
Systems and methods are provided for adaptive equalization, for example for use with coherent optical reception. The equalizer has a loop for updating taps of an adaptive linear filter forming part of the equalizer. In the loop, an error calculator calculates an error, a gradient calculator calculates a gradient of the error or filtered error. One or more gradient filters are used to filter the gradient, and the filtered gradient is used to update the taps of the adaptive linear filter. A reduction in self oscillation in the equalizer is achieved by separating the frequencies with channel features that change with time and scaling them up to speed up convergence of the equalizer.