The present disclosure relates to a concept of nonlinear signal processing which may be used for predistortion for RF power amplifiers. The concept includes generating time variant filter coefficients for a linear filter circuit based on a nonlinear mapping of an input signal, and filtering the input signal with the linear filter circuit using the time variant filter coefficients in order to generate a filtered output signal. Thus, it is proposed to implement a non-linear filter by a time-varying linear filter where the time-varying coefficients are derived from the input signal.

An adaptive identification system, for identifying a propagation system characteristic by an adaptive filter, includes a signal generator that generates an identification input signal including a frequency component of an integer multiple of a fundamental frequency and having a periodicity satisfying a PE condition, a setting unit that sets moving average time to a fundamental period of the identification input signal, and an adaptive algorithm execution unit that uses a moving average value and a diagonal matrix to update a coefficient of the adaptive filter, the moving average value being obtained by calculating a moving average of a cross-correlation vector of a vector of the identification input signal and an observation signal with the moving average time, and the diagonal matrix being obtained by diagonalizing a matrix obtained by calculating a moving average of an autocorrelation matrix of the vector of the identification input signal with the moving average time.

An adaptive identification system, for identifying a propagation system characteristic by an adaptive filter, includes a signal generator that generates an identification input signal including a frequency component of an integer multiple of a fundamental frequency and having a periodicity satisfying a PE condition, a setting unit that sets moving average time to a fundamental period of the identification input signal, and an adaptive algorithm execution unit that uses a moving average value and a diagonal matrix to update a coefficient of the adaptive filter, the moving average value being obtained by calculating a moving average of a cross-correlation vector of a vector of the identification input signal and an observation signal with the moving average time, and the diagonal matrix being obtained by diagonalizing a matrix obtained by calculating a moving average of an autocorrelation matrix of the vector of the identification input signal with the moving average time.

In a nonlinear signal filtering system, a signal having a series of signal samples is filtered. The signal samples are affected by interactions with adjacent signal samples and nonlinear distortions. The system contains a series of alternating linear system elements and nonlinear system elements that are used for mitigation of distortion resulting from the nonlinear distortions with memory effects. The linear system elements can scale each signal sample in the series of signal samples by scaling parameters and sums a plurality of consecutive scaled signal samples, and the nonlinear system elements can transform the output of the linear system elements according to instantaneous nonlinear functions.

The present disclosure relates to a concept of nonlinear signal processing which may be used for predistortion for RF power amplifiers. The concept includes generating time variant filter coefficients for a linear filter circuit based on a nonlinear mapping of an input signal, and filtering the input signal with the linear filter circuit using the time variant filter coefficients in order to generate a filtered output signal. Thus, it is proposed to implement a non-linear filter by a time-varying linear filter where the time-varying coefficients are derived from the input signal.

In a nonlinear signal filtering system, a signal having a series of signal samples is filtered. The signal samples are affected by interactions with adjacent signal samples and nonlinear distortions. The system contains a series of alternating linear system elements and nonlinear system elements that are used for mitigation of distortion resulting from the nonlinear distortions with memory effects. The linear system elements can scale each signal sample in the series of signal samples by scaling parameters and sums a plurality of consecutive scaled signal samples, and the nonlinear system elements can transform the output of the linear system elements according to instantaneous nonlinear functions.

In a nonlinear signal filtering system, a signal having a series of signal samples is filtered. The signal samples are affected by interactions with adjacent signal samples and nonlinear distortions. The system contains a series of alternating linear system elements and nonlinear system elements that are used for mitigation of distortion resulting from the nonlinear distortions with memory effects. The linear system elements can scale each signal sample in the series of signal samples by scaling parameters and sums a plurality of consecutive scaled signal samples, and the nonlinear system elements can transform the output of the linear system elements according to instantaneous nonlinear functions.