Daniel D. Silveira

A New Digital Predictive Predistorter for Behavioral Power Amplifier Linearization

This letter presents a new digital adaptive predistorter (PD) for power amplifier (PA) linearization based on a nonlinear auto-regressive moving average (NARMA) structure. The distinctive characteristic of this PD is its straightforward deduction from the NARMA PA model, without the need of using an indirect learning approach to identify the PD function. The PD itself presents a NARMA structure, and hence it can be quickly implemented by means of lookup tables. Single and multicarrier modulated signals collected from a three-stage LDMOS class AB PA, with a maximum output power of 48-dBm CW have been used to validate the linearity performance of this new predictive predistorter

Efficient Power Amplifier Identification Using Modified Parallel Cascade Hammerstein Models

The automatic generation of behavioral amplifier models based on the measurement of the input and output signals imply demanding requirements for the identification algorithm. In addition to the computational efficiency it is very important that such an algorithm needs only a low number of predefined parameters for the fitting process. To fulfil these requirements we suggest the use of a modified parallel cascade Hammerstein model (MPCHM). To verify the performance of the presented algorithm the behavior of a 3-stage RF power amplifier (PA) was identified. The resulting model was validated using different measurements with similar statistical properties. Furthermore the static nonlinear contributions were deembedded from both the measured and the modeled output signals to visualize the capability of the MPCHM in predicting the amplifier memory effects

Heuristic Algorithms for Power Amplifier Behavioral Modeling

This letter presents the use of two heuristic search algorithms, named simulated annealing and genetic algorithms, for the extraction of power amplifier (PA) behavioral model parameters. Their application in this letter consists in determining the memory length and the most significant delays of the considered model structure. Two PA behavioral models have been considered: an augmented nonlinear moving average model and a nonlinear auto-regressive moving average model. By using WCDMA signals measured from a three-stage LDMOS class AB PA, both PA models were extracted. Finally, results presenting the advantages of using these heuristic search algorithms are provided.

RF-power amplifier characteristics determination using parallel cascade Wiener models and pseudo-inverse techniques

This article presents a black-box modeling approach of a microwave power amplifier (PA) and defines the linear and nonlinear operation regions. An in-band quasi-white real-valued noise like signal is used as stimulus for the identification process to excite every possibly source of nonlinearity. A segment of the input-output measurement data is processed to generate an initial parallel cascade Wiener model (PCWM). The first order Volterra kernel is extracted in order to estimate the amplifiers memory. Then pseudo-inverse techniques are used to find a parsimonious model. This model is cross-validated with the entire measurement data. The presented modeling approach results in a model intended to be numerically robust and having a high identification percentage based on a variance figure of merit. Finally the linear and nonlinear operation regions of the amplifier are defined, as an alternative to the use of AM/AM curves.

Analysis of Variations of Volterra Series Models for RF Power Amplifiers

This article discusses several different implementations of Volterra series models, and performs a fair comparison between them using measured data and well known figures of merit. Among the implemented models, an approach that uses sub-sampling techniques together with Volterra series in a parallel cascade structure shows an improved performance compared to the other variations of Volterra series models. Based on the results, new trends in RF power amplifier behavioral modeling are suggested.

Extraction and Improvements of a Behavioral Model Based on the Wiener-Bose Structure Used for Baseband Volterra Kernels Estimation

In this paper a strategy to extract a first-zone filtered behavioral model based on Wiener-Bose structure, which has a similar structure to the Volterra series, is introduced, and improvements applied to the model identification are highlighted. Considerations regarding the Volterra kernels symmetry for complex signals are described. A closed form for determining the number of independent parameters for a baseband Volterra series was developed and applied in the Wiener-Bose model parametrization process. A method to improve the Hessian condition number in the model calculation procedure is presented. The modeling and validation results based on measurements for an industry standard signal (WCDMA) are displayed. It is shown that this model is capable to represent efficiently RF-power amplifier (PA) memory effects with good accuracy.

Analysis of RF-Power Amplifier Modeling Performance using a 16-QAM Modulation over AWGN Channels

This paper presents the black-box modeling of a microwave power amplifier (PA) and its performance in the nonlinear operation region - low input back off (IBO). As input signal a rectangle 16-QAM corrupted by different levels of additive white Gaussian noise (AWGN) was used. The nonlinearities caused by the amplifier in low IBO operating conditions can change the output constellation and lead to difficulties in finding the correct model parameters, as the black-box identification procedure is based on measured input/output signals. These signals can be altered by different types of noise. The AWGN degrades the signal to noise ratio (SNR) and can lower the model accuracy that should ideally not be influenced. In this paper the models were first estimated using noise-free data (NFD) and secondly with different levels of noisy corrupted data (NCD). NFD was also applied to the models calculated with NCD and vice versa. An investigation of the loss of accuracy caused by the noise in the identified models is presented. Three situations were considered: 6dB IBO, 4dB IBO, and 2dB IBO. It is shown that the pseudo-inverse based models present reasonable results for PA modeling when AWGN is incident

A numerical and experimental study of the remote long-period grating fiber sensor with Raman Amplification

In this paper, we analyze numerically and experimentally a remote optical sensor system based on Raman Amplification, composed by one long period grating operating as a sensor head separated by 50 km from the optical source and the interrogation unit composed by two fiber Bragg gratings. Since the active components of the system and the sensor head are separated over such a large distance, it is necessary to consider optical amplification to strengthen the optical signal. The use of Raman amplification allows reaching the desirable gain bandwidth by changing the pump lasers parameters such as the power, number of pumps and spectral position. We present the obtained measurement results of the environmental temperature for two different setups that was analyzed a priori by the numerical model. We show that the power ratio between the two central wavelengths of the FBG has a linear relation with the change of LPG resonance with temperature.

A novel experimental set-up for turbidity sensing based on plastic optical fibre

Abstract A novel set-up for remote sensing the turbidity of a solution using plastic optical fibres together with a mirror is proposed. The authors have estimated turbidity of a liquid in terms of the nephelometric turbidity unit – NTU, a standard unit that relates the solution appearance with its turbidity for two proposed set-ups. Its performance was also investigated using clay sample measurements over a concentration range of 0–10 g/L. The proposed set-ups are useful for detection of suspended particles in a solution even in small quantities due to its high sensitivity, simplicity and robustness.

PMD Emulator based on PZTs attached to pieces of an Hi-Bi fiber

In this paper we demonstrate the development of an adjustable and compact polarization mode dispersion (PMD) emulator device, based on piezoelectric actuators that squeezes a defined pieces of an Hi-Bi fiber. Using this device, the first and second order PMD (SOPMD) can be reproduced in a controlled and compact way, allowing its use as a reliable metrological reference for PMD compensators and for the study of several polarizations phenomena that can represent impairments to the high data transmission networks. This emulator was used to perform an analysis of the correlation between the PMD and SOPMD under different climate chamber temperature.