A method for configuring a data transmission network , executed by a configuration device , wherein said data transmission network comprises a transmitter , a receiver with a communication channel between said transmitter and said receiver, said method comprising: training (S1) a machine learning model of said data transmission network, wherein said 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 successive messages; generating a group of transmission symbols for each message in said sequence of successive messages using said transmitter neural network; concatenating said groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of said sequence of transmission symbols over said communication channel using said channel model to said receiver; analysing a sequence of received symbols using said reception neural network to generate a decoded message; and updating said machine learning model based on an output of said reception neural network, wherein the method further comprises applying an inter-symbol interference function of said channel model to said sequence of transmission symbols to generate inter-message interference symbols as said sequence of received symbols and configuring the transmitter and the receiver based on the trained machine learning model. training (S1) a machine learning model of said data transmission network, wherein said 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 successive messages; generating a group of transmission symbols for each message in said sequence of successive messages using said transmitter neural network; concatenating said groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of said sequence of transmission symbols over said communication channel using said channel model to said receiver; analysing a sequence of received symbols using said reception neural network to generate a decoded message; and updating said machine learning model based on an output of said reception neural network, wherein the method further comprises applying an inter-symbol interference function of said channel model to said sequence of transmission symbols to generate inter-message interference symbols as said sequence of received symbols and configuring the transmitter and the receiver based on the trained machine learning model. providing a message within a sequence of successive messages; generating a group of transmission symbols for each message in said sequence of successive messages using said transmitter neural network; concatenating said groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of said sequence of transmission symbols over said communication channel using said channel model to said receiver; analysing a sequence of received symbols using said reception neural network to generate a decoded message; and updating said machine learning model based on an output of said reception neural network, wherein the method further comprises applying an inter-symbol interference function of said channel model to said sequence of transmission symbols to generate inter-message interference symbols as said sequence of received symbols and configuring the transmitter and the receiver based on the trained machine learning model. <EOS>