Data transmission network configuration

Abstract

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.