Active control of vibrant actuators with adaptive adjustment of the reference

An improved adaptive filter for eliminating periodic vibrations of unknown frequency in rotary machinery is presented. The proposed canceller is based on a usual bank of digital adaptive notch filters, each filter tuned in the cancellation of one harmonic. The amplitude and phase of each harmonic is adaptively adjusted by an LMS-based algorithm. Moreover, the central frequency of each notch filter is also adaptively adjusted (fine tuning). The resulting algorithm, tested in an industrial application, shows effectiveness in cancelling unknown periodic disturbances, reducing environmental noise and maintenance problems.Peer ReviewedPostprint (published version

Adaptive us-mrac for disturbance cancellation

A variable structure, model reference adaptive control (VS-MRAC) devoted to cancel interferences without the requirement of an auxiliary input is proposed. This method is an improved alternative to the strategies recently proposed in the control theory literature.Peer ReviewedPostprint (published version

An experimental course on digital communications

In this paper a laboratory course on digital communications is presented. This course has been designed for medium degree professionals in the telecommunications field, and it is based on training equipment developed to change the usual theoretical classrooms for laboratory seminars.Peer ReviewedPostprint (published version

Application of hyperstability theory to interference cancelling

An alternative to the usual adaptive noise cancelling method devoted to removing interference is presented. In the conventional methodology to implement adaptive cancellers a reference signal is necessary correlated with the interference. This requirement (not always possible) is a limitation of this kind of canceller. The paper shows the use of model reference adaptive systems (MRAS), designed by using hyperstability theory, in order to cancel interference without the requirement of an auxiliary input. In the proposed methodology it is not necessary to have a reference signal to identify the interference; it is enough to know the upper and lower boundaries of this interference.Peer ReviewedPostprint (published version

Dynamic selection and estimation of the digital predistorter parameters for power amplifier linearization

© © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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 component 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.Peer ReviewedPostprint (author's final draft

3-D distributed memory polynomial behavioral model for concurrent dual-band envelope tracking power amplifier linearization

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a new 3-D behavioral model to compensate for the nonlinear distortion arising in concurrent dual-band (DB) Envelope Tracking (ET) Power Amplifiers (PAs). The advantage of the proposed 3-D distributed memory polynomial (3D-DMP) behavioral model, in comparison to the already published behavioral models used for concurrent dual-band envelope tracking PA linearization, is that it requires a smaller number of coefficients to achieve the same linearity performance, which reduces the overall identification and adaptation computational complexity. The proposed 3D-DMP digital predistorter (DPD) is tested under different ET supply modulation techniques. Moreover, further model order reduction of the 3D-DMP DPD is achieved by applying the principal component analysis (PCA) technique. Experimental results are shown considering a concurrent DB transmission of aWCDMA signal at 1.75GHz and a 10-MHz bandwidth LTE signal at 2.1 GHz. The performance of the proposed 3D-DMP DPD is evaluated in terms of linearity, drain power efficiency, and computational complexity.Peer ReviewedPostprint (author's final draft

Computationally efficient real-time digital predistortion architectures for envelope tracking power amplifiers

This paper presents and discusses two possible real-time digital predistortion (DPD) architectures suitable for envelope tracking (ET) power amplifiers (PAs) oriented at a final computationally efficient implementation in a field programmable gate array (FPGA) device. In ET systems, by using a shaping function is possible to modulate the supply voltage according to different criteria. One possibility is to use slower versions of the original RF signal’s envelope in order to relax the slew-rate (SR) and bandwidth (BW) requirements of the envelope amplifier (EA) or drain modulator. The nonlinear distortion that arises when performing ET with a supply voltage signal that follows both the original and the slow envelope will be presented, as well as the DPD function capable of compensating for these unwanted effects. Finally, two different approaches for efficiently implementing the DPD functions, a polynomial-based and a look-up table-based, will be discussed.Peer ReviewedPostprint (published version

Spectral weighting orthogonal matching pursuit algorithm for enhanced out-of-band digital predistortion linearization

"© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."This paper presents a new variant of the orthogonal matching pursuit (OMP) algorithm for reducing the computational complexity of the digital predistortion (DPD) behavioral model in the forward path. The proposed spectral weighting OMP (SW-OMP) algorithm focuses on selecting the most relevant basis functions to compensate for the out-of-band residual distortion which may eventually be masked by the dominant in-band residual error. This basis selection is carried out in an off-line process that does not affect the computational complexity of the real-time closed-loop DPD but, on the contrary, reduces its complexity while enhancing the robustness. Experimental results show that by selecting the DPD coefficients with the SW-OMP, the inherent ACLR and NMSE degradation suffered when reducing the number of coefficients is mitigated under strong nonlinear operation, when compared to using the basis functions selected by the classical OMP algorithm.Peer ReviewedPostprint (author's final draft

Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In 5G and beyond radios, the increased bandwidth, the fast-changing waveform scenarios, and the operation of large array multiple-input multiple-output (MIMO) transmitter architectures have challenged both the polynomial and the artificial neural network (ANN) MIMO adaptive digital predistortion (DPD) schemes. This article proposes training data selection methods and dimensionality reduction techniques that can be combined to enable relevant reductions of the DPD training time and the implementation complexity for MIMO transmitter architectures. In this work, the combination of an efficient uncorrelated equation selection (UES) mechanism together with orthogonal least squares (OLS) is proposed to reduce the training data length and the number of basis functions at every behavioral modeling matrix in the polynomial MIMO DPD scheme. For ANN MIMO DPD architectures, applying UES and principal component analysis (PCA) is proposed to reduce the input dataset length and features, respectively. The UES-OLS and the UES-PCA techniques are experimentally validated for a 2×2 MIMO test setup with strong power amplifier (PA) input and output crosstalk.This work was supported in part by the MCIN/AEI/10.13039/501100011033 under Project PID2020-113832RB-C22 and Project PID2020-113832RB-C21; and in part by the European Union-NextGenerationEU through the Spanish Recovery, Transformation and Resilience Plan, under Project TSI-063000-2021-121 (MINECO UNICO Programme).Peer ReviewedPostprint (author's final draft

Continuous-time adaptive control applied to rf amplifier linearization

A new approach to the RF power amplifier linearization problem is presented. The proposed solution applies non-linear theories (Lyapunov direct method) to adaptive filtering in order to improve the linearity of the RF amplifiers. The obtained design requires lower circuit complexity than the LINC amplifier, and is not based on iterative algorithms nor sub-system identification. Up to 100 MHz these functions could be implemented, at present, with operational amplifiers and integrated analog multipliers (four quadrants). The adjusting algorithm convergence or the interruption of the communication are not problems in the proposed adaptive solution. The canceller structure design is based on model reference adaptive systems (MRAS): to cancel the error between the plant output (distortion output of the RF amplifier) and reference model (the desired signal obtained from a linear and low-power amplifier) by using continuous-time techniques. The proposed structure is studied by computer simulation (SPICE program) in a class-A RF power amplifier, The behaviour of the adapted amplifier is studied when power transistors approach nonlinear operating zones (saturation state).Peer ReviewedPostprint (published version