The invention relates to a device for linearising a power amplifier by employing digital predistortion, comprising: a digital predistortion module, configured to infer a polar domain predistortion to be applied to a signal, and comprising a first neural network and a second neural network respectively configured to correct amplitude and phase distortion produced by the amplifier; an optimisation module of each of said neural networks configured to implement meta-learning, using: a meta-initialisation providing a prior initialisation of the initial weights of each of said neural networks; a meta-matching of the initial weights into optimal weights of each of said neural networks.

Systems, circuitries, and methods for predistorting a digital signal in a transmit chain based on a predistortion function are provided. A method includes shifting a center frequency of an input signal by an offset to generate an adapted signal; predistorting the adapted signal based on a predistortion function to generate a predistorted adapted signal; reverting the shifting of the center frequency of the predistorted adapted signal by the offset to generate a predistorted signal; and causing transmission of the predistorted signal by a transmit chain.

Systems, circuitries, and methods for predistorting a digital signal in a transmit chain based on a predistortion function are provided. A method includes shifting a center frequency of an input signal by an offset to generate an adapted signal; predistorting the adapted signal based on a predistortion function to generate a predistorted adapted signal; reverting the shifting of the center frequency of the predistorted adapted signal by the offset to generate a predistorted signal; and causing transmission of the predistorted signal by a transmit chain.

Example embodiments relate to machine learning based digital pre-distortion for power amplifiers. A device may amplify a signal with a power amplifier and transmit the signal. The signal may be received by an internal feedback receiver of the device. The device may further comprise a first machine learning model configured to emulate an external feedback receiver and to generate an emulated feedback signal based on the internal feedback signal. The device may further comprise a second machine learning model configured to determine digital pre-distortion parameters for the power amplifier based on the emulated feedback signal. Apparatuses, methods, and computer programs are disclosed.

Example embodiments relate to machine learning based digital pre-distortion for power amplifiers. A device may amplify a signal with a power amplifier and transmit the signal. The signal may be received by an internal feedback receiver of the device. The device may further comprise a first machine learning model configured to emulate an external feedback receiver and to generate an emulated feedback signal based on the internal feedback signal. The device may further comprise a second machine learning model configured to determine digital pre-distortion parameters for the power amplifier based on the emulated feedback signal. Apparatuses, methods, and computer programs are disclosed.

Described examples provide for digital communication circuits and systems that implement digital pre-distortion (DPD). In an example, a system includes a DPD circuit configured to compensate an input signal for distortions resulting from an amplifier. The DPD circuit includes an infinite impulse response (IIR) filter configured to implement a transfer function. The IIR filter includes a selection circuit configured to selectively output a selected parameter. The transfer function is based on the selected parameter.

The invention relates to a device for linearising a power amplifier by employing digital predistortion, comprising: a digital predistortion module, configured to infer a polar domain predistortion to be applied to a signal, and comprising a first neural network and a second neural network respectively configured to correct amplitude and phase distortion produced by the amplifier; an optimisation module of each of said neural networks configured to implement meta-learning, using: a meta-initialisation providing a prior initialisation of the initial weights of each of said neural networks; a meta-matching of the initial weights into optimal weights of each of said neural networks.

Some embodiments herein describe a radio frequency communication system that can include a transmitter to output an radio frequency (RF) transmit signal, the transmitter including a digital pre-distortion system (DPD) that pre-distorts the RF transmit signal. The DPD system can include a configurable non-linear filter, such as a Laguerre filter, having multiple rows where at least one row operates with a configurable decimation ratio. The DPD system can further include decimators and a crossbar switch coupled between the decimators.

A power amplifier linearization circuit and related apparatus is provided. In examples disclosed herein, the power amplifier linearization circuit includes an analog pre-distortion (APD) circuit coupled to an input of a power amplifier. Notably, the power amplifier can exhibit linearity response deviation, namely linearity amplitude response deviation and linearity phase response deviation, when amplifying a radio frequency (RF) signal under a compression condition. As such, the APD circuit is configured to receive a control signal corresponding to the linearity response deviation and pre-process the RF signal based on the control signal before providing the RF signal to the power amplifier. As a result, it may be possible to reduce the linearity response deviation in the power amplifier, thus helping to improve linearity and RF performance of the power amplifier.

Systems, circuitries, and methods for predistorting a digital signal in a transmit chain based on a predistortion function are provided. A method includes shifting a center frequency of an input signal by an offset to generate an adapted signal; predistorting the adapted signal based on a predistortion function to generate a predistorted adapted signal; reverting the shifting of the center frequency of the predistorted adapted signal by the offset to generate a predistorted signal; and causing transmission of the predistorted signal by a transmit chain.