Adaptive Optical Equalizer Initialization for Fractional Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing adaptive equalizer technologies face challenges in determining an appropriate initial tap coefficient value when performing fractional sampling, leading to errors in training sequence synchronization and channel estimation during digital coherent optical transmission.
Innovation Solution
The adaptive equalizer employs a sample buffer and processor to perform fractional sampling, determines the initial tap coefficient using a training sequence with phase-shifted sample values, and updates the tap coefficient based on the specified training sequence, ensuring proper synchronization and channel estimation even with non-integer sampling rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2×-oversampling is performed to avoid aliasing and recover proper waveform, then waveform recovery accuracy is improved, but the number of taps of the equalizer is doubled and processing amount increases
Solution Approach 1:
The patent changes the sampling rate parameter from the conventional 2×-oversampling to fractional sampling (between 1× and 2× symbol rate). This parameter change reduces the number of taps required for the equalizer while maintaining acceptable waveform recovery through the specific training sequence positioning method disclosed in the patent.
2Device complexity
If fractional sampling is used to reduce the number of sampling times, then the number of taps is reduced and circuit scale is downsized, but determining appropriate initial tap coefficient becomes difficult
Solution Approach 1:
The patent applies preliminary action by inserting a training sequence at a specifically positioned point in the data stream before normal data transmission begins. This training sequence is positioned based on fractional sampling considerations, allowing the receiver to pre-determine appropriate initial tap coefficients for the equalizer before actual data reception, thus solving the initialization difficulty.
Solution Approach 2:
The training sequence serves as an intermediary element between the transmitted signal and the equalizer initialization process. By using this intermediate training sequence with known characteristics and specific positioning, the system can derive initial tap coefficients without directly processing unknown data signals, thus facilitating the difficult initialization under fractional sampling.
3Measurement precision
If training sequence is used to determine equalizer initial tap coefficient, then initial value accuracy is improved, but training sequence positioning errors occur under fractional sampling
Solution Approach 1:
The patent changes the positioning parameter of the training sequence specifically for fractional sampling conditions. Instead of using standard positioning methods that work for integer sampling rates, the patent adjusts the training sequence position based on the fractional sampling rate, ensuring that the training sequence aligns correctly with the sampled data points even when the sampling rate is non-integer multiple of symbol rate.
Data Source
AI summary
An adaptive equalizer, includes a sample buffer; and a processor coupled to the sample buffer and configured to: perform an adaptive equalization on data which has been fractionally sampled at a sampling rate higher than once a symbol rate and lower than twice the symbol rate, determine an initial value of a tap coefficient of the adaptive equalizer by using a training sequence inserted in the data, shift, by a predetermined shift amount, a sample point of one pattern from among two consecutive patterns included in the training sequence, specify a position of the training sequence in the data by replacing an original sample value with a sample value at the shifted sample point, and update the initial value of the tap coefficient based on the specified training sequence.