End-to-End Neural Network Training for Data Transmission Networks
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Solution Overview
Problem
Existing neural network approaches for configuring data transmission networks train using single messages, which fails to accurately represent the actual operation of the network, leading to lower than expected performance when deployed in real networks due to the mismatch between the channel model used during training and the real channel.
Innovation Solution
A method is provided to train a machine learning model comprising a transmitter neural network, a channel model, and a receiver neural network by using sequences of messages, simulating transmission over a communication channel, and updating the model based on received symbols to improve performance. This includes applying inter-symbol interference functions to simulate real-world interference and using stochastic gradient descent for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If single messages are used during training, then the training process is simpler and faster, but the performance when deployed in real networks deteriorates due to mismatch between channel model and real channel
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a simplified stochastic channel model before deployment. This preliminary training establishes a baseline performance, and then fine-tuning is performed later to adapt to the specific real channel characteristics, thus resolving the contradiction between fast initial training and eventual real-world performance
Solution Approach 2:
The patent changes the channel model parameters from a generic stochastic model during initial training to a specific real channel model during fine-tuning. By adjusting the parameters to match actual channel conditions (such as path loss, fading characteristics, and interference patterns), the system achieves both efficient training and optimal real-world performance
2Ease of manufacture
If individual functional blocks are designed and optimized separately, then each block can be independently improved, but the end-to-end performance does not achieve optimal results
Solution Approach 1:
The patent merges the optimization process by training the entire transmission network as a unified end-to-end system rather than optimizing individual blocks separately. The neural network encompasses the complete signal processing chain from encoder to decoder, allowing joint optimization that captures interactions between different functional blocks and achieves superior overall performance
Solution Approach 2:
The patent creates a universal optimization framework using a single neural network model that can handle multiple functions (encoding, modulation, filtering, demodulation, decoding) simultaneously. This multi-functional approach allows the system to be trained once to optimize all functions together, rather than requiring separate optimization processes for each functional block
Data Source
AI summary
A method and devices for configuring a data transmission network are disclosed. The method is for configuring a data transmission network, executed by a configuration device, wherein the data transmission network comprises at least one transmitter, at least one receiver with a communication channel between the transmitter and the receiver, the method comprising: training a machine learning model of the data transmission network, wherein the machine learning model comprises at least a transmitter model including a transmitter neural network, a channel model, and a receiver model including a receiver neural network by providing a message within a sequence of messages; generating a group of transmission symbols for each message in the sequence of messages using the transmitter neural network; concatenating the groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of the sequence of transmission symbols over the communication channel using the channel model to the receiver; analysing a sequence of received symbols using the reception neural network to generate a decoded message; and updating the machine learning model based on an output of said reception neural network. In this way, the machine learning model can be trained using representative sequences of message, which improves performance when deployed in a real network.