Optical Communication Neural Network Training With Device Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neural network training methods for optical communication, such as self-training and joint-training, are inefficient and resource-intensive, failing to provide optimal performance in varying channel conditions of point-to-multipoint networks like PON, and require extensive data and iterations to achieve satisfactory accuracy.
Innovation Solution
A method where optical communication devices share feedback information on neural network training performance, allowing an initial parameter set to be updated and optimized across devices, enabling faster and more accurate training of neural networks for signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If self-training or joint-training methods are used for neural network training in optical communication, then training can be performed, but training efficiency is low and resource consumption is high
Solution Approach 1:
The system performs preliminary training at a first optical communication device to obtain optimized initial parameter values before deploying the neural network to second optical communication devices. This preliminary action eliminates the need for extensive training iterations at each receiving device, significantly improving training efficiency and reducing resource consumption across the network.
2Loss of time
If traditional training methods are used, then neural networks can be trained, but training time is extensive and convergence is slow
Solution Approach 1:
The system implements a feedback mechanism where training performance indications from second optical communication devices are transmitted back to the first device. The first device uses this feedback to iteratively optimize the initial parameter values, creating a closed-loop system that accelerates convergence and reduces training time across the network.
Solution Approach 2:
By pre-training the neural network at the first optical communication device to obtain optimized initial parameter values, the system performs the time-consuming training work in advance. This preliminary action dramatically reduces the training time required at individual receiving devices and accelerates overall network deployment.
3Adaptability or versatility
If neural networks are trained independently at each device, then adaptation to local channel conditions is achieved, but training resources and data requirements increase significantly
Solution Approach 1:
The system segments the training process into two phases: centralized pre-training at the first optical communication device to obtain general optimized parameters, and localized fine-tuning at second devices to adapt to specific channel conditions. This segmentation reduces data requirements at each device while maintaining adaptability to local conditions.
Solution Approach 2:
The first optical communication device performs preliminary training using aggregated data to establish optimized initial parameter values. This preliminary action reduces the amount of local data needed at each receiving device, as the network starts from a pre-optimized state rather than requiring extensive local training data.
Data Source
AI summary
The method includes receiving, at a first optical communication device, feedback information on training of a neural network from at least one second optical communication device, the neural network configured to process a signal received from the first optical communication device, the feedback information at least including a training performance indication for training of the neural network conducted at the at least one second optical communication device; updating, based on the feedback information, a first initial parameter value set for the neural network maintained at the first optical communication device, to obtain a second initial parameter value set for the neural network; and transmitting the second initial parameter value set to at least one further second optical communication device, for training of the neural network to be conducted at the at least one further second optical communication device based on the second initial parameter value set.