Universal Neural Network Signal Processing for Optical Line Terminals
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Solution Overview
Problem
Existing neural network-based signal processing in optical communication systems is not universally applicable across different optical network units due to varying hardware configurations, leading to frequent reconfiguration and reloading of NN parameters, resulting in increased overhead and operational delays.
Innovation Solution
A method where the optical line terminal trains neural network-based signal processing devices based on property parameters of each optical network unit, allowing the same signal processing device to be applicable to all units in the network, adjusting signals transmitted from any unit to recover the original signal based on a distorted signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network parameters are frequently reconfigured and reloaded for different optical network units, then signal processing accuracy is improved, but operational delays and overhead increase
Solution Approach 1:
The patent applies universality by training a single neural network model that can universally process signals from multiple optical network units with different hardware configurations. The model learns to adapt to various device characteristics (bandwidth, fiber length, dispersion) through hybrid parameter inputs, eliminating the need for separate models for each ONU and thus reducing reconfiguration overhead and operational delays.
2Measurement precision
If neural network parameters are frequently reconfigured and reloaded for different optical network units, then signal processing accuracy is improved, but system overhead increases
Solution Approach 1:
The patent creates a universal neural network model that serves multiple ONUs simultaneously, reducing the quantity of parameters that need to be transmitted and stored. Instead of maintaining separate parameter sets for each ONU, the system uses a single model with hybrid parameter inputs, significantly reducing system overhead.
Solution Approach 2:
The patent extracts the essential hardware characteristics (bandwidth, fiber length, dispersion) as hybrid parameters from each ONU and feeds them into the universal neural network. This extraction approach allows the model to adapt to different hardware configurations without requiring complete reconfiguration, reducing the overhead of parameter transmission and processing.
3Measurement precision
If separate neural network models are used for each optical network unit, then signal processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent reduces device complexity by implementing a single universal neural network model that can handle multiple ONUs, instead of maintaining separate models for each unit. The universal model accepts hybrid parameters as input and processes signals from any ONU, simplifying the overall system architecture and reducing computational complexity.
4Measurement precision
If neural network parameters are frequently reconfigured for different optical network units, then transmission accuracy is improved, but operational efficiency decreases
Solution Approach 1:
The patent performs preliminary training of the neural network model using hybrid parameters from multiple ONUs during an offline phase. This preliminary action allows the model to learn and adapt to various hardware configurations in advance, so that during online operation, the model can process signals from any ONU without requiring frequent reconfiguration, thereby improving operational efficiency while maintaining transmission accuracy.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, an apparatus for optical communication and computer-readable medium. The method comprises receiving, at an optical line terminal, an access request from a first optical network unit; in accordance with a determination that the first optical network unit is not registered at the optical line terminal, obtaining a first parameter set from the first optical network unit, and updating an association relationship between a distorted signal received at the optical line terminal and an original signal recovered from the distorted signal, based on the first parameter set. In this way, regarding different levels of optical signal quality attenuation caused by optical network units with different channel responses in one passive optical network, a unified signal compensation scheme can be provided for all optical network units, in order to mitigate transmission distortion of signals on an uplink of optical communication and significantly reduce configuration overheads in practice.