In this paper, a new method for dynamically estimating and updating the coefficients of a digital... more In this paper, a new method for dynamically estimating and updating the coefficients of a digital predistortion (DPD) linearizer is presented. By means of the partial least squares (PLS) algorithm, the basis matrix used in the DPD estimation/adaptation is dynamically updated at every iteration to minimize the linearization error. Moreover, only the minimum necessary DPD coefficients being required to meet a target estimation error are computed. The proposed estimation technique is compared with the standard least squares (LS) estimation solved by using QR decomposition. Experimental results show the similar linearization performance obtained with both estimation methods, but in the case of the dynamic PLS, using less coefficients at every iteration. Finally, the proposed algorithm allows a high level of parallelization, which makes it suitable for FPGA implementation.
This paper presents a multi lookup table (LUT) implementation scheme for the 3D distributed memor... more This paper presents a multi lookup table (LUT) implementation scheme for the 3D distributed memory polynomial (3D-DMP) behavioral model used in Digital Predistortion (DPD) linearization for concurrent dual-band envelope tracking (ET) power amplifiers (PAs). The proposed 3D-Distributed Memory LUTs (3D-DML) architecture is suitable for efficient FPGA implementation. In order to optimize the linearization performance as well as to reduce the number of resources of the 3D-DML model, a new variant of the Orthogonal Matching Pursuit (OMP) algorithm is proposed to properly select the best LUTs. Experimental results show that the proposed strategy reduces the number of LUTs (i.e. the number of coefficients) while meeting the targeted linearity levels.
IEEE Transactions on Microwave Theory and Techniques, Dec 1, 2018
This paper presents an estimation/adaptation method based on the adaptive principal component ana... more This paper presents an estimation/adaptation method based on the adaptive principal component analysis (APCA) technique to guarantee the identification of the minimum necessary parameters of a digital predistorter. The proposed estimation/adaptation technique is suitable for online fieldprogrammable gate array (FPGA) or system on chip (SoC) implementation. By exploiting the orthogonality of the resulting transformed matrix obtained with the APCA technique, it is possible to reduce the number of coefficients to be estimated which, at the same time, has a beneficial regularization effect by preventing ill-conditioning or over-fitting problems. Therefore, this identification/adaptation method enhances the robustness of the parameter estimation, simplifies the adaptation by reducing the number of estimated coefficients. Due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results will show that it is possible to determine the minimum number of parameters to be estimated in order to meet the targeted linearity levels while ensuring a robust well-conditioned identification. Moreover, the results will show how thanks to the orthogonality property of the new basis functions, the coefficients of the digital predistorter can be estimated independently. This allows to trade-off the digital predistorter adaptation time versus performance and hardware complexity.
This paper presents a technique for selecting online the coefficients to be updated in a digital ... more This paper presents a technique for selecting online the coefficients to be updated in a digital predistorter (DPD)<br> based on direct learning. The proposed method, which is based on a combination of matching pursuit (MP) and least squares<br> (LS) techniques (and is therefore named MP-LS method) allows to improve the power amplifier (PA) linearization performance<br> of a fixed number of DPD coefficients, due to the fact that at each DPD iteration the coefficients to be updated are properly<br> chosen. The proposed technique is compared to a conventional LS estimation, and experimental results demonstrate that the MP-LS method can provide a performance improvement in relation to a DPD with fixed-preselected coefficients. The method could be<br> especially useful in DPD systems that have hardware restrictions in the resources to be used by the update subsystem in the<br> feedback path. That is the case of DPDs based on FPGA devices implementing ...
This paper presents an overview on how the artificial neural networks (ANN) are applied to digita... more This paper presents an overview on how the artificial neural networks (ANN) are applied to digitally linearize modern transmitters. The use of nonlinear ANNs is intended to either assist or replace the traditional crest factor reduction (CFR) and digital predistortion (DPD) building blocks, and benefit from their inherently good approximation capabilities and reduced hardware complexity when targeting complex transceiver scenarios such as those present in 5G. There is not a universal procedure to set up the best ANN given a specific application. However, in this paper some design considerations which have been experimentally validated in the literature will be summarized both considering single-antenna and multi-antenna transmitters. Finally, some principles in the selection of ANN parameters for nonlinear modeling will be showcased by using a simulation test bench that employs measured data from a strongly non-linear GaN PA operated with wideband signals.
IEEE Transactions on Microwave Theory and Techniques, 2019
This paper presents a new technique that dynamically estimates and updates the coefficients of a ... more This paper presents a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization. The proposed technique is dynamic in the sense of estimating, at every iteration of the coefficient's update, only the minimum necessary parameters according to a criterion based on the residual estimation error. At the first step, the original basis functions defining the DPD in the forward path are orthonormalized for DPD adaptation in the feedback path by means of a precalculated principal components analysis (PCA) transformation. The robustness and reliability of the precalculated PCA transformation (i.e., PCA transformation matrix obtained off-line and only once) is tested and verified. Then, at the second step, a properly modified partial least squares (PLS) method, named dynamic partial least squares (DPLS), is applied to obtain the minimum and most relevant transformed components required for updating the coefficients of the DPD linearizer. The combination of the PCA transformation with the DPLS extraction of components is equivalent to a canonical correlation analysis (CCA) updating solution, which is optimum in the sense of generating components with maximum correlation (instead of maximum covariance as in the case of the DPLS extraction alone). The proposed dynamic extraction technique is evaluated and compared in terms of computational cost and performance with the commonly used QR decomposition approach for solving the least squares (LS) problem. Experimental results show that the proposed method (i.e., combining PCA with DPLS) drastically reduces the amount of DPD coefficients to be estimated while maintaining the same linearization performance.
IEEE Transactions on Microwave Theory and Techniques, 2018
This paper presents a technique to estimate the coefficients of a multi look-up table (LUT) digit... more This paper presents a technique to estimate the coefficients of a multi look-up table (LUT) digital predistortion (DPD) architecture based on the partial least squares (PLS) regression method. The proposed 3-D distributed memory LUTs (3D-DML) architecture is suitable for efficient FPGA implementation and compensates for the distortion arising in concurrent dual-band envelope tracking (ET) power amplifiers (PAs). On the one hand, a new variant of the Orthogonal Matching Pursuit (OMP) algorithm is proposed to properly select only the best LUTs of the DPD function in the forward path and thus reducing the number of required coefficients. On the other hand, the PLS regression method is proposed to address both the regularization problem of the coefficient estimation and, at the same time, reducing the number of coefficients to be estimated in the DPD feedback identification path. Moreover, by exploiting the orthogonality of the PLS transformed matrix, the computational complexity of the parameters' identification can be significantly simplified. Experimental results will prove how it is possible to reduce the DPD complexity (i.e. the number of coefficients) in both forward and feedback paths while meeting the targeted linearity levels. Index Terms-Envelope tracking, digital predistortion, look-up tables, partial least squares, power amplifier, principal component analysis.
2018 IEEE/MTT-S International Microwave Symposium - IMS, 2018
This paper presents a new method, based on the adaptive principal component analysis (APCA) techn... more This paper presents a new method, based on the adaptive principal component analysis (APCA) technique, that iteratively creates and updates an orthogonal data matrix used to estimate the parameters of power amplifier (PA) behavioral models or digital predistortion (DPD) linearizers. Unlike the conventional PCA, the proposed block deflacted APCA (BD-APCA) is an iterative and online method that can be easily implemented in embedded processors. The proposed BD-APCA is designed by properly modifying the well-known complex domain generalized Hebbian algorithm (CGHA). This adaptation method enhances the robustness of the parameter estimation, simplifies the adaptation by reducing the number of estimated coefficients and due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results show that the proposed BD-APCA method is a worthy solution for adaptive, online, reduced-order and robust parameter estimation for PA modeling and DPD.
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