Mourad Djamai

CONTINUOUS-TIME MODEL IDENTIFICATION USING REINITIALIZED PARTIAL MOMENTS - APPLICATION TO POWER AMPLIFIER MODELING

Abstract An application of continuous-time system identification to the modeling of a power amplifier used in mobile communication is proposed. Generally, electronic components modeling uses discrete-time black-box models. In order to keep physical knowledge and easy interpretation of estimated model, a continuous-time transfer function model is chosen to represent amplifier transmittance. Hence, parameter estimation is performed by the reinitialized partial moments method. This estimation method has been chosen because of its insensitivity to initial conditions and rough system a priori knowledge. Then, power amplifier continuous-time model allows immediate physical interpretation to the electronics engineer.

Kalman filtering algorithm for on-line memory polynomial predistortion

A new adaptive technique for digital predistortion is presented. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency power amplifier. Kalman filtering algorithm with sliding time-window is adapted to track the changes in the PA characteristics. Simulation and measurement results, using digital signal processing, are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

Behavioral Modeling and Digital Predistortion of RF Power Amplifiers

Modeling accurately the nonlinear behavior of radio frequency power amplifiers is a key point in the design and development of linearizer. In this paper we presented a predistorsion technique based on DSP circuit and the identification of continuous time model describing the power amplifier associated to quadrature modulator and demodulator circuits. The model is composed by two blocks, the first uses polynomial functions to model the nonlinear amplitude and phase distortion, the second one is based on a MIMO continuous filter to describe the PA dynamics. Parameters are determined using identification method based on output error technique and a pseudo random binary sequence as signal excitation. Using the previous solution, the non-linear inverse function is calculated and implemented in the DSP. Experimental results of digital predistorsion show an improvement of 10 dB of the ACPR for QPSK and pi/4 DQPSK modulated signals

Modeling and identification of continuous-time system for RF amplfiers

In this paper, we present a new identification procedure for radio frequency power amplifier (PA) in the presence of nonlinear distortion and memory effects. The proposed procedure uses a continuous-time model where PA dynamics are modeled with a multivariable filter and a general polynomial function. Using the baseband input and output data, model parameters are obtained by an iterative identification algorithm. Finally, the proposed estimation method is tested and validated on experimental data by comparison of the quadrature IQ signals in time domain.

On-line memory polynomial predistortion based on the adapted kalman filtering algorithm

A new adaptive technique for digital predistortion is presented. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency power amplifier. Kalman filtering algorithm with sliding time-window is adapted to track the changes in the PA characteristics. Simulation and measurement results, using digital signal processing, are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

IDENTIFICATION OF POWER AMPLIFIER USING MIMO TIME-DOMAIN REPRESENTATION

Abstract In this paper, we present a new technique for modeling and characterization of Power Amplifier (PA) by parameter estimation using continuous-time representation. Firstly, we study a continuous model which takes into account nonlinearities and PA filtering. The filter structure includes a non linear polynomial representing amplitude and phase conversion in addition to a MIMO Laplace plant. Then, we propose a new approach for PA model characterization based on parameter estimation with several excitations. Using time-domain measurements, this method deduces recursively an optimal estimation with Non Linear Programming technique. The experimental results show good agreement and demonstrate the possibility of this technique to explain power amplifier dynamics.