Adaptive Average Length Optimization for Coherent Phase Recovery
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Solution Overview
Problem
Existing phase recovery devices in coherent communication systems face challenges in optimizing the average length for phase recovery, as channel noise intensity and carrier phase variation speed are difficult to measure in real-time, affecting the performance of coherent receivers.
Innovation Solution
An adaptive optimization method that calculates the residual phase difference and its auto-correlation value to adjust the average length used in phase recovery, allowing for automatic optimization without prior knowledge of channel or laser characteristics, and enabling low complexity calculations for high-speed receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed average length is used in phase recovery, then the calculation is simple, but the performance deteriorates when channel noise intensity or carrier phase variation speed changes
Solution Approach 1:
The system uses the residual phase difference auto-correlation value to automatically adjust the average length without external intervention. The optimization unit self-regulates the average length parameter based on the calculated auto-correlation, enabling the system to adapt to changing channel conditions autonomously without requiring training sequences or manual configuration.
Solution Approach 2:
The method implements a feedback mechanism where the residual phase difference is calculated, its auto-correlation value is computed, and this value is fed back to adjust the average length. This closed-loop feedback allows the system to continuously optimize the average length based on actual channel conditions, resolving the contradiction between adaptability and complexity.
2Measurement precision
If the average length is increased to reduce noise influence, then phase recovery accuracy improves, but the response speed to phase changes deteriorates
Solution Approach 1:
The average length is transformed from a fixed parameter to a dynamic one that automatically adjusts based on channel conditions. When phase variation is fast, the system decreases the average length to improve response speed. When phase variation is slow and noise is prominent, the system increases the average length to improve accuracy. This dynamic adjustment resolves the contradiction between precision and speed.
3Measurement precision
If real-time detection of channel noise intensity and carrier phase variation speed is performed, then optimal average length can be determined, but the detection complexity and computational load increase significantly
Solution Approach 1:
The method extracts only the essential information needed for optimization - the residual phase difference and its auto-correlation value - from the complex channel conditions. Instead of directly measuring channel noise intensity and carrier phase variation speed, the system extracts these parameters through the auto-correlation calculation, significantly reducing detection complexity while maintaining optimization effectiveness.
Solution Approach 2:
The auto-correlation value serves as an intermediary parameter that mediates between the complex channel conditions and the average length optimization. Rather than directly detecting and using channel noise intensity and phase variation speed, the system uses the auto-correlation value as an intermediate representation that captures the essential characteristics needed for optimization, reducing computational complexity.
4Reliability
If training sequences or prior knowledge of channel characteristics are used, then optimal phase recovery can be achieved, but the system requires additional resources and cannot operate in non-training mode
Solution Approach 1:
The system performs self-optimization using the residual phase difference auto-correlation value without requiring external training sequences or prior knowledge of channel characteristics. The optimization unit automatically adjusts the average length based on the calculated auto-correlation, enabling reliable phase recovery in non-training mode without additional resources or manual configuration.
Data Source
AI summary
The invention provides an average length adaptive optimization method and apparatus. An adaptive optimization method for the average length used for phase recovery comprising: a residual phase difference calculation step, for receiving a current phase of a digital symbol obtained by phase recovery and a data modulation phase of the digital symbol obtained by data recovery, and calculating a residual phase difference of the digital symbol, which is a difference between the current phase and the data modulation phase of the digital symbol; a residual phase difference auto-correlation value calculation step, for calculating an auto-correlation value of the residual phase difference with displacement m, wherein −10≦m≦10, and m is an integral; an optimization step, for optimizing the average length based on the residual phase difference auto-correlation value.