Method and device(s) for supporting machine learning based crest factor reduction and digital predistortion

A method, performed by a device, for supporting performance of machine learning based crest factor reduction (CFR) and/or digital predistortion (DPD) on multiple digital input signals relating to different frequency bands, respectively, in order to signal condition said signals before power amplification and subsequent transmission in said frequency bands by a wireless communication network, the method comprising:

• obtaining said multiple digital input signals as complex valued signals;
• performing feature construction that takes said multiple digital input signals as input and provides constructed feature signals according to predefined constructed feature types as output, wherein said predefined constructed feature types relate to at least the following per complex valued sample of the obtained complex valued multiple digital input signals:
• a real part of the complex valued sample, an imaginary part of the complex valued sample, and at least one of an absolute value of the complex valued sample and a phase of the complex valued sample,
• applying CFR and/or DPD signal conditioning based on one or more trained machine learning models implementing said machine learning based CFR and/or DPD, wherein each of said trained machine learning models takes said constructed feature signals as input, and said trained machine learning models provide CFR and/or DPD first signal conditioned multiple digital output signals as output;
• and
• applying further DPD signal conditioning including a tracking DPD that operates on an error from said machine learning based DPD.