Computer-implemented method for configuring a data transmission system , wherein the data transmission system comprises at least one transmitter , at least one receiver , and a communication channel between the transmitter and the receiver, the method comprising: - training (S1) a machine learning model of the data transmission system , wherein the machine learning model specifies a relationship between input data (s) of the transmitter and output data ( ŝ ) of the receiver , wherein the machine learning model specifies at least a transmitter model , a channel model , and a receiver model including a receiver neural network , wherein the machine learning model is trained as an auto-encoder, - configuring (S2) the transmitter and the receiver based on the trained machine learning model , characterized in that it comprises, after configuring (S2) the transmitter and the receiver based on the trained machine learning model : - obtaining (S3), from the receiver , training data specifying a set of messages sent by the transmitter and the corresponding signals received by the receiver, wherein said training data has been determined by the receiver by: - receiving successive signals (y), - determining a sequence of received messages ( ŝ ) corresponding to the received successive signals, - mapping said sequence of received messages ( ŝ ) to a received codeword (RCW), - determining destination data (û) as a function of the received codeword (RCW), by decoding an error correction code, and - determining a sequence of transmitted messages (s) as a function of the destination data (û), - training (S4) the receiver model based on the obtained training data, and - configuring (S5) the receiver based on the trained receiver model . - training (S1) a machine learning model of the data transmission system , wherein the machine learning model specifies a relationship between input data (s) of the transmitter and output data ( ŝ ) of the receiver , wherein the machine learning model specifies at least a transmitter model , a channel model , and a receiver model including a receiver neural network , wherein the machine learning model is trained as an auto-encoder, - configuring (S2) the transmitter and the receiver based on the trained machine learning model , - obtaining (S3), from the receiver , training data specifying a set of messages sent by the transmitter and the corresponding signals received by the receiver, wherein said training data has been determined by the receiver by: - receiving successive signals (y), - determining a sequence of received messages ( ŝ ) corresponding to the received successive signals, - mapping said sequence of received messages ( ŝ ) to a received codeword (RCW), - determining destination data (û) as a function of the received codeword (RCW), by decoding an error correction code, and - determining a sequence of transmitted messages (s) as a function of the destination data (û), - receiving successive signals (y), - determining a sequence of received messages ( ŝ ) corresponding to the received successive signals, - mapping said sequence of received messages ( ŝ ) to a received codeword (RCW), - determining destination data (û) as a function of the received codeword (RCW), by decoding an error correction code, and - determining a sequence of transmitted messages (s) as a function of the destination data (û), - training (S4) the receiver model based on the obtained training data, and - configuring (S5) the receiver based on the trained receiver model . <EOS>