A method for a flexible and adaptable end-to-end communication using a two-sided machine learning model

An apparatus for wireless communication, comprising:

• at least one memory;
• and
• at least one processor coupled with the at least one memory and operable to cause the apparatus to:
• receive a set of channel output symbols corresponding to an input message and a corresponding input message;
• train a decoder, wherein the decoder comprises at least a portion of an artificial intelligence/machine learning (AI/ML) model configured to generate the input message from the channel output symbols corresponding to the input message, wherein to train the decoder comprises to determine a second set of parameters of the decoder based on an objective function taking the generated input message and the received channel output symbols corresponding to the input message as inputs, wherein the objective function is configured to compute a measure of cross entropy between the generated input message and at least a part of the channel output symbols corresponding to the input message;
• receive a further set of channel output symbols;
• and
• use the trained decoder to decode the further set of channel output symbols and generate an input message corresponding to the further set of channel output symbols.

An apparatus for wireless communication, comprising:

• at least one memory;
• and
• at least one processor coupled with the at least one memory and operable to cause the apparatus to:
• receive or generate a set of channel input symbols corresponding to an input message and a corresponding input message;
• determine or receive channel information representing channel information;
• apply the channel information to the set of channel input symbols to generate a set of channel output symbols corresponding to the input message;
• train a decoder, wherein the decoder comprises at least a portion of an artificial intelligence/machine learning (AI/ML) model configured to generate the input message from the channel output symbols corresponding to the input message, wherein to train the decoder comprises to determine a second set of parameters of the decoder based on an objective function taking the generated input message and the received channel output symbols corresponding to the input message as inputs, wherein the objective function is configured to compute a measure of cross entropy between the generated input message and at least a part of the channel output symbols corresponding to the input message;
• receive a further set of channel output symbols;
• and
• use the trained decoder to decode the further set of channel output symbols and generate an input message corresponding to the further set of channel output symbols.

An apparatus for wireless communication, comprising:

• at least one memory;
• and
• at least one processor coupled with the at least one memory and operable to cause the apparatus to:
• determine whether to update at least a portion of an encoder;
• receive a signal, from a second device, over a communication channel;
• determine information regarding the communication channel from the received signal;
• update a first set of parameters based on the determined information of the communication channel, in response to determining to update an encoder artificial intelligence/machine learning (AI/ML) model;
• use the encoder to encode an input message and generate complex-valued symbols using the updated first parameters;
• and
• transmit, to the second device, the complex-valued symbols.