Abstract: A digital predistortion (DPD) system includes an input configured to receive a DPD input signal. In some embodiments, a non-linear datapath is coupled to the input, where the non-linear datapath includes a plurality of parallel datapath elements each coupled to the input. By way of example, each of the plurality of parallel datapath elements is configured to add a different inverse non-linear component to the DPD input signal corresponding to a non-linear component of an amplifier. In various examples, a first combiner combines an output of each of the plurality of datapath elements to generate a first predistortion signal. In some embodiments, the DPD system further includes a linear datapath coupled to the input in parallel with the non-linear datapath to generate a second predistortion signal. In addition, a second combiner combines the first predistortion signal and the second predistortion signal to generate a DPD output signal.

Abstract: This specification discloses a positioning method and a mobile device. The method is applied to a mobile device having a sensor system and a positioning system, and the method includes: determining that the mobile device is in a preset state; determining a speed reference value of the mobile device based on data detected by the sensor system, and determining a position reference value of the mobile device based on preset map data and an estimated positioning value that is output by the positioning system; and correcting, based on the speed reference value and the position reference value, an estimated speed value and the estimated positioning value that are output by the positioning system. Positioning precision can be improved by using the foregoing technical solution.

Abstract: A crest factor reduction (CFR) system includes a digital tilt filter coupled to an input of the CFR system. In some embodiments, the digital tilt filter is configured to receive a system input signal and generate a digital tilt filter output signal at a digital tilt filter output. In some examples, the CFR system further includes a CFR module coupled to the digital tilt filter output, where the CFR module is configured receive the digital tilt filter output signal and perform a CFR process to the digital tilt filter output signal to generate a CFR module output signal at a CFR module output. In addition, the CFR system may include a digital tilt equalizer coupled to the CFR module output, where the digital tilt equalizer is configured to receive the CFR module output signal and generate a system output signal.

Abstract: A positioning method and a mobile device, where the method is applied to the mobile device and includes periodically detecting a status of the mobile device, where the status of the mobile device includes a driving state and a walking state, positioning the mobile device using a first positioning algorithm when detecting that a quantity of times that the driving state consecutively occurs reaches M, where the M is an integer greater than one, and positioning the mobile device using a second positioning algorithm when detecting that a quantity of times that the walking state consecutively occurs reaches N, where the N is an integer greater than one. Hence, the status of the mobile device can be more accurately determined by implementing this technical solution to position the mobile device using a positioning algorithm matching an actual status of the mobile device, thereby improving accuracy of a positioning result.

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 digi

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 digi

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: 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 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.