26results about "Memory effect compensation" patented technology

According to embodiments, a method performed by an electronic device includes: identifying a channel bandwidth; identifying one or more active DPD units among a plurality of DPD units of a digital pre-distortion (DPD) circuit based on the channel bandwidth and amount of resource allocated within the channel bandwidth; generating a DPD output signal of the DPD circuit based on the one or more active DPD units; generating an amplifier output signal based on the DPD output signal of the DPD circuit, a digital-to-analog converter (DAC), and a power amplifier (PA). While the DPD output signal is generated, at least one DPD unit different from the one or more active DPD units among the plurality of DPD units of the DPD circuit is deactivated.

Systems and methods for compensating a transmit signal for charge trapping effects of a power amplifier are provided. In certain embodiments, a non-linear filter is trained based on time aligning a first set of observations taken from digital transmit data prior to conversion to a radio frequency transmit signal, and a second set of observations taken from an output of a power amplifier that amplifies the radio frequency transmit signal. In certain implementations, the first set of observations and the second set of observations are obtained without decimation. Rather, decimation is provided after timing alignment. By implementing the DPD system in this manner, signal data is not lost by decimation and more accurate timing alignment between the sets of observations is achieved.

A power amplification system (20) has the following features: a power amplifier (11) configured to amplify a first WLAN signal in a first frequency band; a power amplifier (12) configured to amplify a second WLAN signal in a second frequency band higher than the first frequency band; and a (DPD) circuit (311) configured to pre-distort the second WLAN signal without pre-distorting the first WLAN signal.

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 pre-compensation method for a pre-compensation circuit coupled to a power amplifier to compensate for nonlinearity of the power amplifier includes performing pre-distortion according to at least one parameter or at least one hyperparameter to convert a first input signal received by the pre-compensation circuit into a first pre-distortion output signal, updating the at least one parameter or the at least one hyperparameter according to Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization, and performing pre-distortion according to the at least one parameter updated or the at least one hyperparameter updated to convert a second input signal received by the pre-compensation circuit into a second pre-distortion output signal.

This electronic device comprises a power amplifier (PA), one or more processors connected to the PA, and a memory for storing instructions. When executed individually or collectively by the one or more processors, the instructions instruct the electronic device to: monitor input data of the PA and output data of the PA; identify a weighted value for a first digital pre-distortion (DPD); identify a hyperparameter for a second DPD on the basis of estimated input data of the PA estimated on the basis of the second DPD based on a neural network (NN) scheme, estimated input data of the PA estimated on the basis of a first DPD, and the input data of the PA; correct, on the basis of the weighted value and the first DPD, first nonlinear data included in the input data of the PA; and correct, on the basis of the hyperparameter and the second DPD, second nonlinear data included in the input data of the PA.

A device may include a first model implemented using a first processing circuit configured to implement a transformation function to generate an initial predistortion signal based on an input signal corresponding to an output of a power amplifier. The device may include a second model implemented using a second processing circuit configured to implement a modification function to modify the initial predistortion signal. A device may include a configurable multiplier configured to weight an output of the second model based at least in part on a weighting value to obtain a weighted output. A device may include a combiner configured to combine the initial predistortion signal that is output by the first model with the weighted output to generate a digital predistortion signal for the power amplifier in communication with the digital predistortion system.

A power amplifier system front end measures both forward and reverse power associated with an RF transmit signal. A processor is configured to use measurements derived from the measured forward and reverse power output to adjust the RF transmit signal in order to compensate for one or more memory effects of the power amplifier system.

Systems, devices, and methods are provided for using model architecture search for hardware configurations. The method includes receiving, by a computer-implemented system, information associated with a pool of processing units, receiving, by the computer-implemented system, a dataset associated with a data transformation operation, training a parameterized model associated with the data transformation operation based on the dataset and the information associated with the pool of processing units, where the training includes updating at least one parameter of the parameterized model associated with configuring at least a subset of the processing units in the pool, and outputting one or more configurations for at least a subset of the processing units in the pool based on the training.

This invention relates to distortion compensation devices and methods, computer-readable storage media, and communication devices. One provided is a distortion compensation device that uses a distortion compensation model to compensate for distortion in a signal to be amplified by an amplifier, wherein internal states affecting distortion characteristics in the amplifier change, and the distortion compensation model includes: a plurality of computational models having corresponding distortion compensation characteristics for the amplifier in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal states.

A device may include a first model implemented using a first processing circuit configured to implement a transformation function to generate an initial predistortion signal based on an input signal corresponding to an output of a power amplifier. The device may include a second model implemented using a second processing circuit configured to implement a modification function to modify the initial predistortion signal. A device may include a configurable multiplier configured to weight an output of the second model based at least in part on a weighting value to obtain a weighted output. A device may include a combiner configured to combine the initial predistortion signal that is output by the first model with the weighted output to generate a digital predistortion signal for the power amplifier in communication with the digital predistortion system.

A power amplification system is provided that includes a power amplifier configured to amplify a first WLAN signal in a first frequency band, a power amplifier configured to amplify a second WLAN signal in a second frequency band higher than the first frequency band, and a circuit configured to pre-distort the second WLAN signal and to not pre-distort the first WLAN signal.

Aspects of this disclosure relate to Neural Volterra actuators. In an embodiment, a Neural Volterra actuator includes a first processing block that includes an artificial neural network, a second processing block that includes non-linear gain blocks, multipliers, a connection circuit configured to adjust an electrical connection to an input of a multiplier of the multipliers, and a combiner that generates a combined output signal based on output signals from the multipliers. The combined output signal can be a digitally predistorted version of an input signal received by the Neural Volterra actuator. Related methods and systems are also disclosed.

According to embodiments, a method performed by an electronic device includes: identifying a channel bandwidth; identifying one or more active DPD units among a plurality of DPD units of a digital pre-distortion (DPD) circuit based on the channel bandwidth and amount of resource allocated within the channel bandwidth; generating a DPD output signal of the DPD circuit based on the one or more active DPD units; generating an amplifier output signal based on the DPD output signal of the DPD circuit, a digital-to-analog converter (DAC), and a power amplifier (PA). While the DPD output signal is generated, at least one DPD unit different from the one or more active DPD units among the plurality of DPD units of the DPD circuit is deactivated.

Systems, methods and apparatus for processing signals are described. The method includes determining a predistortion signal corresponding to an input signal by processing the input signal through a multi-stage predistortion model that is based on neural network processing and generating a transmission waveform by processing the predistortion signal through transmission electronics.

The invention relates to a neural worthland system for digital pre-distortion. Aspects of the present disclosure relate to a neural wolfram actuator. In an embodiment, a neural Wollaston actuator comprises: a first processing block comprising an artificial neural network; a second processing block including a nonlinear gain block; a multiplier; a connection circuit configured to adjust an electrical connection to an input of a multiplier of the multipliers; and a combiner that generates a combined output signal based on the output signal from the multiplier. The combined output signal may be a digitally pre-distorted version of the input signal received by the Neural Wollaston actuator. Related methods and systems are also disclosed.

There is provided techniques for operating a DPD for a non-linear electronic device. The DPD is using coefficients and basis functions defined by an input signal and tap delays. The method is performed by the DPD. The method comprises receiving the input signal as destined to be input to a non-linear electronic device. The method comprises determining the coefficients and the tap delays for one of the basis functions for the DPD as a function of the input signal. The DPD implements a first control loop configured to determine the tap delays and a second control loop, different from the first control loop, configured to determine the coefficients. The method comprises obtaining an output signal. The output signal is obtained by the DPD pre-distorting the input signal using the determined coefficients and tap delays. The method comprises providing the output signal as input to the non-linear electronic device.

A pre-compensation method for a pre-compensation circuit coupled to a power amplifier to compensate for nonlinearity of the power amplifier includes performing pre-distortion according to at least one parameter or at least one hyperparameter to convert a first input signal received by the pre-compensation circuit into a first pre-distortion output signal, updating the at least one parameter or the at least one hyperparameter according to Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization, and performing pre-distortion according to the at least one parameter updated or the at least one hyperparameter updated to convert a second input signal received by the pre-compensation circuit into a second pre-distortion output signal.

A digital pre-distortion achieving near ideal spectral regrowth suppression with a bandlimited loopback. To eliminate mismatch between the bandwidth of the pre-distortion model terms and the bandwidth of the signal used to determine the model a band limiting operation may be applied to both the loopback signal and the input to the power amplifier used for estimating the pre-distortion coefficients. In addition, oversampling may be performed in the digital domain to avoid aliasing in the nonlinear terms of the pre-distortion model.

A digital pre-distortion achieving near ideal spectral regrowth suppression with a bandlimited loopback. To eliminate mismatch between the bandwidth of the pre-distortion model terms and the bandwidth of the signal used to determine the model a band limiting operation may be applied to both the loopback signal and the input to the power amplifier used for estimating the pre-distortion coefficients. In addition, oversampling may be performed in the digital domain to avoid aliasing in the nonlinear terms of the pre-distortion model.

An electronic device includes a power amplifier (PA), one or more processors coupled with the PA, and memory storing instructions. The instructions cause the electronic device to monitor input data of the PA and output data of the PA, identify a weight value for a first digital pre-distortion (DPD) scheme, identify a hyperparameter for the second DPD scheme, based on estimated input data of the PA estimated based on a second DPD scheme which is based on a neural network (NN) scheme and estimated input data of the PA estimated based on the first DPD scheme, and the input data of the PA, correct first non-linear data included in the input data of the PA based on the weight value and the first DPD scheme, and correct second non-linear data included in the input data of the PA based on the hyperparameter and the second DPD scheme.

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.

There is provided mechanisms for enabling linearization of a non-linear electronic device. A method is performed by a linearizer device. The method comprises receiving an input signal destined to be input to the non-linear electronic device. Input-output characteristics of the non-linear electronic device is in the linearizer device represented by a linearization function defined by a LUT based model of base functions. The linearizer device is configured to in a greedy pursuit framework select the base functions according to a signal reconstruction criterion. The method comprises obtaining an output signal by subjecting the input signal to the linearization function. The method comprises providing the output signal, instead of the input signal, as input to the non-linear electronic device, thereby enabling linearization of the non-linear electronic device.

This disclosure provides an apparatus and method for compensating for nonlinearity in a power amplifier. An apparatus configured to perform wireless communication to accelerate a neural network includes: a predistortion circuit configured to generate a predistorted input signal by performing predistortion on an input signal based on a parameter set including multiple coefficients; a power amplifier configured to generate an output signal by amplifying an RF signal based on the predistorted input signal; and a parameter acquisition circuit configured to obtain second memory polynomial modeling information corresponding to an operating frequency band based on first memory polynomial modeling information corresponding to each of a plurality of frequency bands, and to use the second memory polynomial modeling information to obtain a parameter set according to an indirect learning structure.

The invention relates to a power amplification system, a power amplification circuit and a power amplification method. A power amplification system (20) is provided with: a power amplifier (11) configured so as to amplify a first WLAN signal of a first frequency band; a power amplifier (12) configured to amplify a second WLAN signal in a second frequency band higher than the first frequency band; and a DPD circuit (311) configured so as to predistort the second WLAN signal without predistorting the first WLAN signal.

Aspects of this disclosure relate to Neural Volterra actuators. In an embodiment, a Neural Volterra actuator includes a first processing block that includes an artificial neural network, a second processing block that includes non-linear gain blocks, multipliers, a connection circuit configured to adjust an electrical connection to an input of a multiplier of the multipliers, and a combiner that generates a combined output signal based on output signals from the multipliers. The combined output signal can be a digitally predistorted version of an input signal received by the Neural Volterra actuator. Related methods and systems are also disclosed.