-This paper presents a comparative study on the suitability of using Hammerstein or Wiener models to identify the power amplifier (PA) nonlinear behavior considering memory effects. This comparative takes into account the operational complexity regarding the identification process as well as their accuracy to follow the PA behavior. Both identified PA models will be used to estimate a Hammerstein based predistorter in order to see which model combination provides better linearization results. In addition, two adaptive algorithms for predistorting both PA models are compared in terms of accuracy and converge speed.

Abstract-This paper presents a hardware implementation of a digital predistorter (DPD) for linearizing RF power amplifiers (PAs) for wideband applications. The proposed predistortion linearizer is based on a nonlinear auto-regressive moving average (NARMA) structure, which can be derived from the NARMA PA behavioral model and then mapped into a set of scalable lookup tables (LUTs). The linearizer takes advantage of its recursive nature to relax the LUT count needed to compensate memory effects in PAs. Experimental support is provided by the implementation of the proposed NARMA DPD in a field-programmable gate-array device to linearize a 170-W peak power PA, validating the recursive DPD NARMA structure for W-CDMA signals and flexible transmission bandwidth scenarios. To the best of the authors' knowledge, it is the first time that a recursive structure is experimentally validated for DPD purposes. In addition to the results on PA efficiency and linearity, this paper addresses many practical implementation issues related to the use of FPGA in DPD applications, giving an original insight on actual prototyping scenarios. Finally, this study discusses the possibility of further enhancing the overall efficiency by degrading the PA operation mode, provided that DPD may be unavoidable due to the impact of memory effects.Index Terms-Digital predistortion (DPD), field programmable gate array (FPGA), nonlinear auto-regressive moving average (NARMA) models, power amplifier (PA) linearization.

This paper presents how to apply order reduction in wide-band digital predistortion (DPD) linearizers using the principal component analysis (PCA) technique. This method is tested in a wireless backhauling transmitter where four 28 MHz adjacent subcarrier transmission of M-QAM signals are considered. The DPD has to counteract not only the PA nonlinear behavior, but also its dynamics. This may results critical when considering wideband signals since the number of coefficients required to model memory effects can grow dramatically. By applying the PCA technique, the number of essential parameters can be significantly reduced. In addition, a strategy to minimize the computational cost of finding the optimal coefficients is also presented. A test-bed for evaluating the DPD linearization performance of the RF subsystem when PCA is applied was deployed and experimental results are presented in this paper.

Abstract-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.Index Terms-Amplifier distoriton, digital predistorter (PD), digital radio, direct learning approach, linearization, microwave power amplifiers (PAs), nonlinear auto-regressive moving average (NARMA), radio transmitters.

-This paper sets out a study of the autopilot design for fixed wing UAVs taking into account the aircraft stability, and also the power consumption as a function of the selected control strategy. To provide some generality to the outcomes of this study, construction of a reference small-UAV model, based on averaging the main aircraft defining parameters, is proposed. Using such a reference model of small, fixed-wing UAVs, different control strategies are assessed especially with a view towards enlarging the controllers' sampling time. A beneficial consequence of this sample time enlargement is that the clock rate of the UAV autopilots may be proportionally reduced. This reduction in turn leads directly to decreased electrical power consumption. Such energy saving becomes proportionally relevant as the size and power of the UAV decreases, with benefits of lengthening battery life and, therefore, the flight endurance. Additionally, through the averaged model, derived from both published data and computations made from actual data captured from real UAVs, it is shown that behavior predictions beyond that of any particular UAV model may be extrapolated.