Deep Neuron Network for Single-Step Nonlinearity Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for nonlinearity compensation in optical fiber communications, such as digital backpropagation and perturbation-based algorithms, are complex and inefficient, particularly in wavelength-division-multiplexing systems, and fail to effectively compensate for fiber nonlinearity across long distances.
Innovation Solution
A low-complexity, single-step nonlinearity compensation method using artificial intelligence implemented in a deep neuron network (DNN) that takes PBA triplets and transmitted/received symbols as inputs, automatically constructing optimum coefficients to minimize training loss and estimate nonlinearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital backpropagation or perturbation-based algorithms are used for nonlinearity compensation, then compensation effectiveness is improved, but device complexity increases significantly
Solution Approach 1:
The patent transforms the nonlinearity compensation problem from solving complex differential equations (digital backpropagation) or iterative perturbation calculations to a direct parameter estimation problem using neural networks. The DNN learns optimal parameters (weights and biases) during training that directly map input signals to compensated outputs, eliminating the need for complex iterative computations while maintaining compensation effectiveness.
Solution Approach 2:
The patent replaces the traditional mechanical/mathematical computation system (digital backpropagation algorithms requiring multiple forward-backward propagations) with an artificial intelligence system (deep neural network) that performs compensation in a single forward pass. This substitution leverages the parallel processing capability of neural networks to achieve the same compensation goal with significantly reduced computational steps.
2Measurement precision
If multiple-step compensation algorithms are used, then nonlinearity compensation accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary training of the deep neural network offline using labeled training data that captures various nonlinearity conditions. During this preliminary phase, the network learns optimal compensation strategies for different scenarios. When deployed, the pre-trained network can directly apply learned compensation without requiring multiple iterative steps, thus achieving high accuracy with single-step processing during actual operation.
Solution Approach 2:
The patent creates a virtual model of the nonlinear channel through the deep neural network during training. This copied representation of the channel characteristics allows the system to predict and compensate for nonlinearity effects directly without repeatedly simulating the physical propagation process multiple times, thereby reducing processing time while maintaining accuracy.
3Adaptability or versatility
If conventional DSP architectures are used, then system compatibility is maintained, but nonlinearity compensation performance is limited
Solution Approach 1:
The patent designs a universal deep neural network architecture that can be integrated into existing DSP systems while providing enhanced nonlinearity compensation capabilities. The DNN module serves multiple functions: it compensates for nonlinearity, adapts to different channel conditions through its learned parameters, and maintains compatibility with standard DSP interfaces. This multi-functional design allows conventional systems to achieve improved performance without complete architectural overhaul.
Data Source
AI summary
Aspects of the present disclosure describe a method for digital coherent transmission systems that advantageously provides low-complexity, single-step nonlinearity compensation based on artificial intelligence (AI) implemented in a deep neuron network (DNN).