Methods and apparatuses for perfoming digital predistortion using a combination model

A method of performing digital predistortion, DPD, to provide a transmit signal, x̆(n) wherein the transmit signal, x̆(n), is for deriving one or more amplifier signals, x̆m(n), for driving one or more power amplifiers, wherein the one or more power amplifiers are associated with a respective one or more antenna elements, the method comprising:

• receiving a first signal, x(n);
• inputting the first signal, x(n), into a combination model, wherein the combination model comprises a machine learning, ML, model and a memory polynomial, MP, model, and the MP model is configured to receive an output of the ML model or the ML model is configured to receive an output of the MP model;
• outputting the transmit signal x̆(n), from the combination model;
• receiving a feedback signal, y(n), based on an output of the one or more power amplifiers;
• during a first time period performing training of the ML model using the feedback signal, y(n) and the first signal x(n);
• during the first time period disabling training of the MP model;
• during a second time period performing training of the MP model using the feedback signal, y(n) and the first signal x(n);
• and
• during the second time period disabling training of the ML model.

A DPD module for performing digital predistortion, DPD, to provide a transmit signal, x̆(n) wherein the transmit signal, x̆(n), is for deriving one or more amplifier signals, x̆m(n), for driving one or more power amplifiers wherein the one or more power amplifiers are associated with a respective one or more antenna elements, wherein the combination model comprises a ML model and a MP model, the DPD module comprising processing circuitry configured to:

• receive a first signal, x(n);
• and
• input the first signal x(n) into a combination model, wherein the combination model comprises a machine learning, ML, model and a memory polynomial, MP, model and the MP model is configured to receive an output of the ML model or the ML model is configured to receive an output of the MP model;
• and
• output the transmit signal x̆(n) from the combination model;
• receive a feedback signal, y(n), based on an output of the one or more power amplifiers;
• during a first time period perform training of the ML model using the feedback signal, y(n) and the first signal x(n);
• during the first time period disable training of the MP model;
• during a second time period perform training of the MP model using the feedback signal, y(n) and the first signal x(n);
• and
• during the second time period disable training of the ML model.

A DPD module for training a combination module to perform digital predistortion, DPD, on a first signal x(n) input into the combination model to provide a transmit signal, x̆(n) wherein the transmit signal, x̆(n), is for deriving one or more amplifier signals, x̆m(n), for driving one or more power amplifiers, wherein the one or more power amplifiers are associated with a respective one or more antenna elements, wherein the combination module comprises a machine learning, ML, model and a memory polynomial, MP, model, and the MP model is configured to receive an output of the ML model or the ML model is configured to receive an output of the MP model, the DPD module comprising processing circuitry configured to:

• receive a feedback signal, y(n), based on an output of the one or more power amplifiers;
• train the ML model during a first time period using the feedback signal y(n) and the first signal x(n);
• disable training of the MP model during the first time period;
• train the MP model during a second time period using the feedback signal y(n) and the first signal x(n);
• disable training of the ML model during the second time period.