Abstract:
Example embodiment of the systems and methods of linear impairment modeling to improve digital pre-distortion adaptation performance includes a DPD module that is modified during each sample by a DPD adaptation engine. A linear impairment modeling module separates the linear and non-linear errors introduced in the power amplifier. The linear impairment model is adjusted during each sample using inputs from the input signal and from a FB post processing module. The linear impairment modeling module removes the linear errors such that the DPD adaptation engine only adapts the DPD module based on the non-linear errors. This increases system stability and allows for the correction of IQ imbalance inside the linear impairment modeling, simplifying the feedback post-processing.
Abstract:
A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).
Abstract:
A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).
Abstract:
A digital pre-distortion component includes: a first capturing component that captures a first sample set of data; a first generating component that generates a first change matrix associated with a portion of the first sample set of data; a first memory component that stores the first change matrix; a second capturing component that captures a second sample set of data; a second generating component that generates a second change matrix associated with a portion of the second sample set of data; a second memory component that stores the second change matrix; a third capturing component that captures a third sample set of data; a third generating component that generates a third change matrix associated with a portion of the third sample set of data; a comparing component that compares the third change matrix with the first change matrix to obtain a first comparison, and compares the third change matrix with the second change matrix to obtain a second comparison; and an adapting component that adapts the digital pre-distortion component with the third sample set of data based on one of the first comparison and the second comparison.
Abstract:
A digital pre-distortion component includes: a first capturing component that captures a first sample set of data; a first generating component that generates a first change matrix associated with a portion of the first sample set of data; a first memory component that stores the first change matrix; a second capturing component that captures a second sample set of data; a second generating component that generates a second change matrix associated with a portion of the second sample set of data; a second memory component that stores the second change matrix; a third capturing component that captures a third sample set of data; a third generating component that generates a third change matrix associated with a portion of the third sample set of data; a comparing component that compares the third change matrix with the first change matrix to obtain a first comparison, and compares the third change matrix with the second change matrix to obtain a second comparison; and an adapting component that adapts the digital pre-distortion component with the third sample set of data based on one of the first comparison and the second comparison.
Abstract:
Example embodiment of the systems and methods of linear impairment modeling to improve digital pre-distortion adaptation performance includes a DPD module that is modified during each sample by a DPD adaptation engine. A linear impairment modeling module separates the linear and non-linear errors introduced in the power amplifier. The linear impairment model is adjusted during each sample using inputs from the input signal and from a FB post processing module. The linear impairment modeling module removes the linear errors such that the DPD adaptation engine only adapts the DPD module based on the non-linear errors. This increases system stability and allows for the correction of IQ imbalance inside the linear impairment modeling, simplifying the feedback post-processing.
Abstract:
A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).
Abstract:
A method of dynamically calculating and updating the Volterra kernels used by the Digital Pre Distortion engine based on output power, input signal bandwidth, multicarrier configuration, frequency response and power amplifier temperature. A dominant Volterra kernels searching DSP engine based on innovation bases with minimum RMS error selection is implemented to continuously update the Volterra kernels set used in DPD to model the power amplifier non linear behaviors.
Abstract:
A method of dynamically calculating and updating the Volterra kernels used by the Digital Pre Distortion engine based on output power, input signal bandwidth, multicarrier configuration, frequency response and power amplifier temperature. A dominant Volterra kernels searching DSP engine based on innovation bases with minimum RMS error selection is implemented to continuously update the Volterra kernels set used in DPD to model the power amplifier non linear behaviours.