28results about "Amplitude adaptive predistortion" patented technology

Embodiments described herein relate to methods and apparatuses 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. A method comprises: 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 outputting the transmit signal x̆(n), from the combination model.

This application relates to a predistortion circuit for a wireless transmitter and a method for generating a predistorted baseband signal. Specifically, the invention discloses a predistortion circuit for a wireless transmitter, comprising a signal input configured to receive a baseband signal. Furthermore, the predistortion circuit includes a predistorter configured to generate a predistorted baseband signal using the baseband signal and a selection of a first predistorter configuration and a second predistorter configuration.

A digital pre-distortion circuit and technique for a signal being amplified. Digital pre-distortion is performed using a model-based pre-distortion function. Pre-distortion is further corrected using a data-driven pre-distortion function. The model-based pre-distortion function and the data-driven pre-distortion function are trained using training data including a digital loopback signal generated by sampling a loopback signal of an amplifier, the digital pre-distorted signal input to the amplifier for a time period, and an intermediate signal output from the model-based pre-distortion function driven by the digital loopback signal or an output of the data-driven pre-distortion function. Parameters may be continually updated in order to account for changes in characteristics of the amplifier.

A predistortion circuit for a wireless transmitter includes a signal input configured to receive a baseband signal. Further, the predistortion circuit includes a predistorter configured to generate a predistorted baseband signal using the baseband signal and a select of one of a first predistorter configuration and a second predistorter configuration.

Example embodiments relate to machine learning based power amplifier digital predistortion. A device can utilize a power amplifier to amplify and transmit a signal. The signal can be received by an internal feedback receiver of the device. The device can further include a first machine learning model configured to emulate an external feedback receiver and generate an emulated feedback signal based on the internal feedback signal. The device can further include a second machine learning model configured to determine a digital predistortion parameter for the power amplifier based on the emulated feedback signal. Apparatuses, methods, and computer programs are disclosed.

The present disclosure relates to a system (108) and a method (700) for improving linearity of a power amplifier (PA) (312). The system (108) includes a processing engine (210) connected to an inverse coefficient estimation (ICE) module (212). The ICE module (212) is designed to capture one or more output data samples of the power amplifier (PA) (312), process the one or more output data samples to generate an error signal indicating power amplifier non-linearity, and estimate one or more parameters of a pre-distorter using a periodic prognostic error convergence (PPEC) technique based on the error signal. The estimation of pre-distorter one or more parameters involves estimating inverse coefficients using a time-varying forgetting factor λ. The ICE module (212) generates a pre-distorted input signal using the estimated one or more parameters and applies the pre-distorted input signal to the PA (312) for improving linearity of the power amplifier (PA) (312).

An adaptive digital pre-distortion device includes a legacy digital pre-distortion (DPD) device, one or more processors including processing circuitry, and a memory storing instructions. The instructions, when executed by the one or more processors individually or collectively, cause the adaptive DPD device to receive a plurality of system parameters, estimate, using a neural network, one or more coefficients of the legacy DPD device, and apply the one or more coefficients to the legacy DPD device. The neural network is configured to generate, as outputs, the one or more coefficients based on the plurality of system parameters.

Systems, devices, and methods relating to hybrid basis function, neural network-based digital predistortion (DPD) are provided. An exemplary apparatus for a radio frequency (RF) transceiver includes a digital predistortion (DPD) actuator for receiving an input signal associated with a nonlinear component of the RF transceiver and outputting a predistorted signal. The DPD actuator includes a basis function-based actuator for performing a first DPD operation using a set of basis functions associated with a first nonlinear characteristic of the nonlinear component. The DPD actuator further includes a neural network-based actuator for performing a second DPD operation using a first neural network associated with a second nonlinear characteristic of the nonlinear component. The predistorted signal is based on a first output signal of the basis function-based actuator and a second output signal of the neural network-based actuator.

Embodiments described herein relate to methods and apparatuses for performing digital predistortion, DPD, to provide a transmit signal, x(n) wherein the transmit signal, x(n), is for deriving one or more amplifier signals, xm(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. A method comprises: 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 outputting the transmit signal x(n), from the combination model.

A circuit (100) for compensating for nonlinearity without essentially changing an operating point of a characteristic curve and / or an operating range of a characteristic curve is described, the circuit (100) comprising an alternating voltage signal source (10) for providing an input signal (20), a control unit (30) for receiving the input signal (20) and converting the input signal (20) into a predistorted signal (40) according to at least one predefined predistortion parameter, and a sink (50) for receiving the predistorted signal (40), the sink (50) being adapted to provide an adjustment signal to the sink (50) for operating the sink (50) in an operating range or at an operating point. and the control unit (30) is configured to receive at least one sensor signal (70) of the sink (50) in a feedback manner and to adapt at least one preset pre-distortion parameter based on the at least one sensor signal (70), the control unit (30) converting the input signal (20) into a pre-distorted signal (40) by the at least one adapted pre-distortion parameter in order to provide the pre-distorted signal (40) to the sink (50) without essentially changing an operating point of the characteristic curve and / or an operating range of the characteristic curve. Additionally, a method for compensating for nonlinearity without essentially changing an operating point of the characteristic curve and / or an operating range of the characteristic curve is described.

According to an aspect, an apparatus may comprise a digital pre-distorter, DPD, circuitry including at least one first-order infinite impulse response, IIR, filter and at least one second-order IIR filter. The apparatus may further comprise a power amplifier, at least one processor and at least one memory comprising instructions which, when executed by the at least one processor, cause the apparatus at least to compensate for non-linearities in the power amplifier by using the at least one first-order IIR filter and the at least one second-order IIR filter.

A signal transmitter for transmitting signals in a radio frequency (RF) domain is provided. The transmitter comprises circuitry configured to receive a source RF signal in the RF domain and produce a pre-distortion RF signal in the RF domain with a phase and a magnitude dependent on a phase and a magnitude of the source RF signal. The circuitry is further configured to combine the source RF signal and the pre-distortion RF signal to produce a combined RF signal and amplify the combined RF signal to produce an amplified RF signal. The circuitry outputs the amplified RF signal through a suitable transmission medium. An extremum seeking controller determines the phase and amplitude of the pre-distortion RF signal based on the phase and amplitude of the source RF signal.

An adaptive digital pre-distortion device includes a legacy digital pre-distortion (DPD) device, one or more processors including processing circuitry, and a memory storing instructions. The instructions, when executed by the one or more processors individually or collectively, cause the adaptive DPD device to receive a plurality of system parameters, estimate, using a neural network, one or more coefficients of the legacy DPD device, and apply the one or more coefficients to the legacy DPD device. The neural network is configured to generate, as outputs, the one or more coefficients based on the plurality of system parameters.

Embodiments described herein relate to methods and apparatuses for performing digital predistortion, DPD, to provide a transmit signal, z(n) wherein the transmit signal, z(n), is for deriving one or more amplifier signals, zm(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. A method comprises: 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 outputting the transmit signal z(n), from the combination model.

A circuit comprises an input, an amplifier circuit, signal amplitude detection apparatus, and a predistorter. The input is configured to receive an input signal. The amplifier circuit is configured to output an amplified signal. The signal amplitude detection apparatus is configured to detect at least a portion of a backward wave which interferes with the amplified signal. The signal amplitude detection apparatus is further configured to generate a signal proportional to an amplitude and / or a power of the at least a portion of the backward wave to obtain a generated signal. The predistorter is configured to modify the input signal based on the generated signal and to provide a modified signal to the amplifier circuit.

A signal transmitter for transmitting signals in a radio frequency (RF) domain is provided. The transmitter comprises circuitry configured to receive a source RF signal in the RF domain and produce a pre-distortion RF signal in the RF domain with a phase and a magnitude dependent on a phase and a magnitude of the source RF signal. The circuitry is further configured to combine the source RF signal and the pre-distortion RF signal to produce a combined RF signal and amplify the combined RF signal to produce an amplified RF signal. The circuitry outputs the amplified RF signal through a suitable transmission medium. An extremum seeking controller determines the phase and amplitude of the pre-distortion RF signal based on the phase and amplitude of the source RF signal.

According to an aspect, an apparatus may comprise a digital pre-distorter, DPD, circuitry including at least one first-order infinite impulse response, IIR, filter and at least one second-order IIR filter. The apparatus may further comprise a power amplifier, at least one processor and at least one memory comprising instructions which, when executed by the at least one processor, cause the apparatus at least to compensate for non-linearities in the power amplifier by using the at least one first-order IIR filter and the at least one second-order IIR filter.

Embodiments described herein relate to methods and apparatuses for performing digital predistortion, DPD, to provide a transmit signal, x(n) wherein the transmit signal, x(n), is for deriving one or more amplifier signals, xm(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. A method comprises: 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 outputting the transmit signal x(n), from the combination model.

Embodiments herein disclose a method for controlling a non-linear effect of a power amplifier by an apparatus. The method includes acquiring an input data of the power amplifier of the apparatus and an output data of the power amplifier. Further, the method includes determining an inverse function using a neural network. The inverse function maps normalized output data of the PA to the input data of the PA, where the neural network comprises at least one sub-network for at least one memory tap from a plurality of memory taps in the neural network. Further, the method includes modifying the input data based on the determined inverse function value by dynamically changing a usage of the at least one memory tap from the plurality of memory taps. Further, the method includes compensating the non-linear effect in the output data of the power amplifier.

The present subject matter relates to a method for amplifying a radio frequency (RF) signal for a power amplification system comprising multiple amplifying devices, the method comprising: determining a configuration of the power amplification system based on power values related to the RF signal, wherein the configuration indicates an activation-deactivation pattern of the amplifying devices of the power amplification system for amplification of the RF signal, controlling the power amplification system according to the determined configuration for amplifying the RF signal.

Described are a circuit for compensating non-linearities essentially without changing a characteristic curve operating point and / or operating range, having: an alternating voltage signal source for providing an input signal; a control unit which receives the input signal and converts it to a predistorted signal depending on at least one preset predistortion parameter; and a sink for receiving the predistorted signal, the sink being coupled to an adjusting unit configured to provide the sink with an adjusting signal, to operate the sink in an operating range or at an operating point, the control unit being configured to receive at least one sensor signal of the sink in a feedback manner and to adapt the at least one preset predistortion parameter based on the at least one sensor signal, and a corresponding method.

A digital pre-distortion circuit and technique for a signal being amplified. Digital pre-distortion is performed using a model-based pre-distortion function. Pre-distortion is further corrected using a data-driven pre-distortion function. The model-based pre-distortion function and the data-driven pre-distortion function are trained using training data including a digital loopback signal generated by sampling a loopback signal of an amplifier, the digital pre-distorted signal input to the amplifier for a time period, and an intermediate signal output from the model-based pre-distortion function driven by the digital loopback signal or an output of the data-driven pre-distortion function. Parameters may be continually updated in order to account for changes in characteristics of the amplifier.

The invention relates to selective activation of an amplification device of a power amplification system. The present subject matter relates to a method for amplifying a radio frequency (RF) signal for a power amplification system comprising a plurality of amplification devices, the method comprising: determining a configuration of the power amplification system based on a power value associated with the RF signal, where the configuration indicates an activation-deactivation mode of the amplification devices of the power amplification system for amplifying the RF signal, the power amplification system is controlled according to the determined configuration for amplifying the RF signal.

The present subject matter relates to a method for amplifying a radio frequency (RF) signal for a power amplification system comprising multiple amplifying devices, the method comprising: determining a configuration of the power amplification system based on power values related to the RF signal, wherein the configuration indicates an activation-deactivation pattern of the amplifying devices of the power amplification system for amplification of the RF signal, controlling the power amplification system according to the determined configuration for amplifying the RF signal.

Disclosed are an artificial intelligence-based digital pre-distortion device and method for improving nonlinearity of a power amplifier. The artificial intelligence-based digital pre-distortion method for improving nonlinearity of a power amplifier, according to an embodiment of the present application, may comprise the steps of: (a) collecting training data including input signal characteristics and output signal characteristics of the power amplifier; (b) constructing an artificial intelligence model for predicting and correcting nonlinear distortion of the power amplifier by using the training data, and mounting, in an on-device manner, the artificial intelligence model on a communication device including the power amplifier; (c) inputting characteristic information of a real-time signal associated with the power amplifier into the artificial intelligence model; and (d) deriving a distortion correction value for the real-time signal by using the artificial intelligence model, and correcting the real-time signal on the basis of the derived distortion correction value.

An adaptive digital pre-distortion apparatus, an electronic apparatus, and an operation method of the electronic apparatus are provided. The adaptive digital pre-distortion device comprises: a legacy digital pre-distortion (DPD) device; one or more processors including processing circuitry; and a memory storing instructions. The instructions, when executed individually or collectively by the one or more processors, cause the adaptive DPD device to: receive a plurality of system parameters; estimating one or more coefficients of the legacy DPD device using a neural network; and applying the one or more coefficients to the legacy DPD device. The neural network is configured to generate the one or more coefficients as an output based on the plurality of system parameters.

The present disclosure provides a method of linearization for a non-linear system, comprising: a group of steps comprising: inputting (302) an input signal to the non-linear system; obtaining (304) an output signal from the non-linear system in response to the input signal being input to the non-linear system; obtaining (306) a desired signal for the non-linear system; comparing (308) the output signal with the desired signal, to determine (310) whether the input signal is a target signal for the non-linear system to achieve a desired performance; and in response to determining (310) that the input signal is not the target signal for the non-linear system to achieve the desired performance, generating (316) an updated signal in frequency domain by applying a coefficient to eliminate a difference between the output signal and the desired signal, for updating the input signal to the non-linear system. The present disclosure also provides a corresponding device, computer programs, and computer-readable storage.

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.