Independent digital predistortion parameters estimation using adaptive principal component analysis

©2018 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 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 field-programmable gate array or system on chip 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 overfitting problems. Therefore, this identification/adaptation method enhances the robustness of the parameter estimation and 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 tradeoff the digital predistorter adaptation time versus performance and hardware complexity.Peer ReviewedPostprint (author's final draft

A fast engineering approach to high efficiency power amplifier linearization for avionics applications

This PhD thesis provides a fast engineering approach to the design of digital predistortion (DPD) linearizers from several perspectives: i) enhancing the off-line training performance of open-loop DPD, ii) providing robustness and reducing the computational complexity of the parameters identification subsystem and, iii) importing machine learning techniques to favor the automatic tuning of power amplifiers (PAs) and DPD linearizers with several free-parameters to maximize power efficiency while meeting the linearity specifications. One of the essential parts of unmanned aerial vehicles (UAV) is the avionics, being the radio control one of the earliest avionics present in the UAV. Unlike the control signal, for transferring user data (such as images, video, etc.) real-time from the drone to the ground station, large transmission rates are required. The PA is a key element in the transmitter chain to guarantee the data transmission (video, photo, etc.) over a long range from the ground station. The more linear output power, the better the coverage or alternatively, with the same coverage, better SNR allows the use of high-order modulation schemes and thus higher transmission rates are achieved. In the context of UAV wireless communications, the power consumption, size and weight of the payload is of significant importance. Therefore, the PA design has to take into account the compromise among bandwidth, output power, linearity and power efficiency (very critical in battery-supplied devices). The PA can be designed to maximize its power efficiency or its linearity, but not both. Therefore, a way to deal with this inherent trade-off is to design high efficient amplification topologies and let the PA linearizers take care of the linearity requirements. Among the linearizers, DPD linearization is the preferred solution to both academia and industry, for its high flexibility and linearization performance. In order to save as many computational and power resources as possible, the implementation of an open-loop DPD results a very attractive solution for UAV applications. This thesis contributes to the PA linearization, especially on off-line training for open-loop DPD, by presenting two different methods for reducing the design and operating costs of an open-loop DPD, based on the analysis of the DPD function. The first method focuses on the input domain analysis, proposing mesh-selecting (MeS) methods to accurately select the proper samples for a computationally efficient DPD parameter estimation. Focusing in the MeS method with better performance, the memory I-Q MeS method is combined with feature extraction dimensionality reduction technique to allow a computational complexity reduction in the identification subsystem by a factor of 65, in comparison to using the classical QR-LS solver and consecutive samples selection. In addition, the memory I-Q MeS method has been proved to be of crucial interest when training artificial neural networks (ANN) for DPD purposes, by significantly reducing the ANN training time. The second method involves the use of machine learning techniques in the DPD design procedure to enlarge the capacity of the DPD algorithm when considering a high number of free parameters to tune. On the one hand, the adaLIPO global optimization algorithm is used to find the best parameter configuration of a generalized memory polynomial behavioral model for DPD. On the other hand, a methodology to conduct a global optimization search is proposed to find the optimum values of a set of key circuit and system level parameters, that properly combined with DPD linearization and crest factor reduction techniques, can exploit at best dual-input PAs in terms of maximizing power efficiency along wide bandwidths while being compliant with the linearity specifications. The advantages of these proposed techniques have been validated through experimental tests and the obtained results are analyzed and discussed along this thesis.Aquesta tesi doctoral proporciona unes pautes per al disseny de linealitzadors basats en predistorsió digital (DPD) des de diverses perspectives: i) millorar el rendiment del DPD en llaç obert, ii) proporcionar robustesa i reduir la complexitat computacional del subsistema d'identificació de paràmetres i, iii) incorporació de tècniques d'aprenentatge automàtic per afavorir l'auto-ajustament d'amplificadors de potència (PAs) i linealitzadors DPD amb diversos graus de llibertat per poder maximitzar l’eficiència energètica i al mateix temps acomplir amb les especificacions de linealitat. Una de les parts essencials dels vehicles aeris no tripulats (UAV) _es l’aviònica, sent el radiocontrol un dels primers sistemes presents als UAV. Per transferir dades d'usuari (com ara imatges, vídeo, etc.) en temps real des del dron a l’estació terrestre, es requereixen taxes de transmissió grans. El PA _es un element clau de la cadena del transmissor per poder garantir la transmissió de dades a grans distàncies de l’estació terrestre. A major potència de sortida, més cobertura o, alternativament, amb la mateixa cobertura, millor relació senyal-soroll (SNR) la qual cosa permet l’ús d'esquemes de modulació d'ordres superiors i, per tant, aconseguir velocitats de transmissió més altes. En el context de les comunicacions sense fils en UAVs, el consum de potència, la mida i el pes de la càrrega útil són de vital importància. Per tant, el disseny del PA ha de tenir en compte el compromís entre ample de banda, potència de sortida, linealitat i eficiència energètica (molt crític en dispositius alimentats amb bateries). El PA es pot dissenyar per maximitzar la seva eficiència energètica o la seva linealitat, però no totes dues. Per tant, per afrontar aquest compromís s'utilitzen topologies amplificadores d'alta eficiència i es deixa que el linealitzador s'encarregui de garantir els nivells necessaris de linealitat. Entre els linealitzadors, la linealització DPD és la solució preferida tant per al món acadèmic com per a la indústria, per la seva alta flexibilitat i rendiment. Per tal d'estalviar tant recursos computacionals com consum de potència, la implementació d'un DPD en lla_c obert resulta una solució molt atractiva per a les aplicacions UAV. Aquesta tesi contribueix a la linealització del PA, especialment a l'entrenament fora de línia de linealitzadors DPD en llaç obert, presentant dos mètodes diferents per reduir el cost computacional i augmentar la fiabilitat dels DPDs en llaç obert. El primer mètode se centra en l’anàlisi de l’estadística del senyal d'entrada, proposant mètodes de selecció de malla (MeS) per seleccionar les mostres més significatives per a una estimació computacionalment eficient dels paràmetres del DPD. El mètode proposat IQ MeS amb memòria es pot combinar amb tècniques de reducció del model del DPD i d'aquesta manera poder aconseguir una reducció de la complexitat computacional en el subsistema d’identificació per un factor de 65, en comparació amb l’ús de l'algoritme clàssic QR-LS i selecció de mostres d'entrenament consecutives. El segon mètode consisteix en l’ús de tècniques d'aprenentatge automàtic pel disseny del DPD quan es considera un gran nombre de graus de llibertat (paràmetres) per sintonitzar. D'una banda, l'algorisme d’optimització global adaLIPO s'utilitza per trobar la millor configuració de paràmetres d'un model polinomial amb memòria generalitzat per a DPD. D'altra banda, es proposa una estratègia per l’optimització global d'un conjunt de paràmetres clau per al disseny a nivell de circuit i sistema, que combinats amb linealització DPD i les tècniques de reducció del factor de cresta, poden maximitzar l’eficiència de PAs d'entrada dual de gran ample de banda, alhora que compleixen les especificacions de linealitat. Els avantatges d'aquestes tècniques proposades s'han validat mitjançant proves experimentals i els resultats obtinguts s'analitzen i es discuteixen al llarg d'aquesta tesi

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version

Compact Digital Predistortion for Multi-band and Wide-band RF Transmitters

This thesis is focusing on developing a compact digital predistortion (DPD) system which costs less DPD added power consumptions. It explores a new theory and techniques to relieve the requirement of the number of training samples and the sampling-rate of feedback ADCs in DPD systems. A new theory about the information carried by training samples is introduced. It connects the generalized error of the DPD estimation algorithm with the statistical properties of modulated signals. Secondly, based on the proposed theory, this work introduces a compressed sample selection method to reduce the number of training samples by only selecting the minimal samples which satisfy the foreknown probability information. The number of training samples and complex multiplication operations required for coefficients estimation can be reduced by more than ten times without additional calculation resource. Thirdly, based on the proposed theory, this thesis proves that theoretically a DPD system using memory polynomial based behavioural modes and least-square (LS) based algorithms can be performed with any sampling-rate of feedback samples. The principle, implementation and practical concerns of the undersampling DPD which uses lower sampling-rate ADC are then introduced. Finally, the observation bandwidth of DPD systems can be extended by the proposed multi-rate track-and-hold circuits with the associated algorithm. By addressing several parameters of ADC and corresponding DPD algorithm, multi-GHz observation bandwidth using only a 61.44MHz ADC is achieved, and demonstrated the satisfactory linearization performance of multi-band and continued wideband RF transmitter applications via extensive experimental tests

Bandwidth utilization maximization of scientific RF communication systems

A method for more efficiently utilizing the frequency bandwidth allocated for data transmission is presented. Current space and range communication systems use modulation and coding schemes that transmit 0.5 to 1.0 bits per second per Hertz of radio frequency bandwidth. The goal in this LDRD project is to increase the bandwidth utilization by employing advanced digital communications techniques. This is done with little or no increase in the transmit power which is usually very limited on airborne systems. Teaming with New Mexico State University, an implementation of trellis coded modulation (TCM), a coding and modulation scheme pioneered by Ungerboeck, was developed for this application and simulated on a computer. TCM provides a means for reliably transmitting data while simultaneously increasing bandwidth efficiency. The penalty is increased receiver complexity. In particular, the trellis decoder requires high-speed, application-specific digital signal processing (DSP) chips. A system solution based on the QualComm Viterbi decoder and the Graychip DSP receiver chips is presented

Digital Front-End Signal Processing with Widely-Linear Signal Models in Radio Devices

Necessitated by the demand for ever higher data rates, modern communications waveforms have increasingly wider bandwidths and higher signal dynamics. Furthermore, radio devices are expected to transmit and receive a growing number of different waveforms from cellular networks, wireless local area networks, wireless personal area networks, positioning and navigation systems, as well as broadcast systems. On the other hand, commercial wireless devices are expected to be cheap, be relatively small in size, and have a long battery life. The demands for flexibility and higher data rates on one hand, and the constraints on production cost, device size, and energy efficiency on the other, pose difficult challenges on the design and implementation of future radio transceivers. Under these diametric constraints, in order to keep the overall implementation cost and size feasible, the use of simplified radio architectures and relatively low-cost radio electronics are necessary. This notion is even more relevant for multiple antenna systems, where each antenna has a dedicated radio front-end. The combination of simplified radio front-ends and low-cost electronics implies that various nonidealities in the remaining analog radio frequency (RF) modules, stemming from unavoidable physical limitations and material variations of the used electronics, are expected to play a critical role in these devices. Instead of tightening the specifications and tolerances of the analog circuits themselves, a more cost-effective solution in many cases is to compensate for these nonidealities in the digital domain. This line of research has been gaining increasing interest in the last 10-15 years, and is also the main topic area of this work. The direct-conversion radio principle is the current and future choice for building low-cost but flexible, multi-standard radio transmitters and receivers. The direct-conversion radio, while simple in structure and integrable on a single chip, suffers from several performance degrading circuit impairments, which have historically prevented its use in wideband, high-rate, and multi-user systems. In the last 15 years, with advances in integrated circuit technologies and digital signal processing, the direct-conversion principle has started gaining popularity. Still, however, much work is needed to fully realize the potential of the direct-conversion principle. This thesis deals with the analysis and digital mitigation of the implementation nonidealities of direct-conversion transmitters and receivers. The contributions can be divided into three parts. First, techniques are proposed for the joint estimation and predistortion of in-phase/quadrature-phase (I/Q) imbalance, power amplifier (PA) nonlinearity, and local oscillator (LO) leakage in wideband direct-conversion transmitters. Second, methods are developed for estimation and compensation of I/Q imbalance in wideband direct-conversion receivers, based on second-order statistics of the received communication waveforms. Third, these second-order statistics are analyzed for second-order stationary and cyclostationary signals under several other system impairments related to circuit implementation and the radio channel. This analysis brings new insights on I/Q imbalances and their compensation using the proposed algorithms. The proposed algorithms utilize complex-valued signal processing throughout, and naturally assume a widely-linear form, where both the signal and its complex-conjugate are filtered and then summed. The compensation processing is situated in the digital front-end of the transceiver, as the last step before digital-to-analog conversion in transmitters, or in receivers, as the first step after analog-to-digital conversion. The compensation techniques proposed herein have several common, unique, attributes: they are designed for the compensation of frequency-dependent impairments, which is seen critical for future wideband systems; they require no dedicated training data for learning; the estimators are computationally efficient, relying on simple signal models, gradient-like learning rules, and solving sets of linear equations; they can be applied in any transceiver type that utilizes the direct-conversion principle, whether single-user or multi-user, or single-carrier or multi-carrier; they are modulation, waveform, and standard independent; they can also be applied in multi-antenna transceivers to each antenna subsystem separately. Therefore, the proposed techniques provide practical and effective solutions to real-life circuit implementation problems of modern communications transceivers. Altogether, considering the algorithm developments with the extensive experimental results performed to verify their functionality, this thesis builds strong confidence that low-complexity digital compensation of analog circuit impairments is indeed applicable and efficient

Modeling Approaches for Active Antenna Transmitters

The rapid growth of data traffic in mobile communications has attracted interest to Multiple-Input-Multiple-Output (MIMO) communication systems at millimeter-wave (mmWave) frequencies. MIMO systems exploit active antenna arrays transmitter configurations to obtain higher energy efficiency and beamforming flexibility. The analysis of transmitters in MIMO systems becomes complex due to the close integration of several antennas and power amplifiers (PAs) and the problems associated with heat dissipation. Therefore, the transmitter analysis requires efficient joint EM, circuit, and thermal simulations of its building blocks, i.e., the antenna array and PAs. Due to small physical spacing at mmWave, bulky isolators cannot be used to eliminate unwanted interactions between PA and antenna array. Therefore, the mismatch and mutual coupling in the antenna array directly affect PA output load and PA and transmitter performance. On the other hand, PAs are the primary source of nonlinearity, power consumption, and heat dissipation in transmitters. Therefore, it is crucial to include joint thermal and electrical behavior of PAs in analyzing active antenna transmitters. In this thesis, efficient techniques for modeling active antenna transmitters are presented. First, we propose a hardware-oriented transmitter model that considers PA load-dependent nonlinearity and the coupling, mismatch, and radiated field of the antenna array. The proposed model is equally accurate for any mismatch level that can happen at the PA output. This model can predict the transmitter radiation pattern and nonlinear signal distortions in the far-field. The model\u27s functionality is verified using a mmWave active subarray antenna module for a beam steering scenario and by performing the over-the-air measurements. The load-pull modeling idea was also applied to investigate the performance of a mmWave spatial power combiner module in the presence of critical coupling effects on combining performance. The second part of the thesis deals with thermal challenges in active antenna transmitters and PAs as the main source of heat dissipation. An efficient electrothermal modeling approach that considers the thermal behavior of PAs, including self-heating and thermal coupling between the IC hot spots, coupled with the electrical behavior of PA, is proposed. The thermal model has been employed to evaluate a PA DUT\u27s static and dynamic temperature-dependent performance in terms of linearity, gain, and efficiency. In summary, the proposed modeling approaches presented in this thesis provide efficient yet powerful tools for joint analysis of complex active antenna transmitters in MIMO systems, including sub-systems\u27 behavior and their interactions

Temperature-dependent Characterization of Power Amplifiers Using an Efficient Electrothermal Analysis Technique

In this paper, we propose an efficient methodology for the electrothermal characterization of power amplifier (PA) integrated circuits. The proposed electrothermal analysis method predicts the effect of temperature variations on the key performances of PAs, such as gain and linearity, under realistic dynamic operating conditions. A comprehensive technique for identifying an equivalent compact thermal model, using data from 3-D finite element method thermal simulation and nonlinear curve fitting algorithms, is described. Two efficient methods for electrothermal analysis applying the developed compact thermal model are reported. The validity of the methods is evaluated using commercially available electrothermal computer-aided design (CAD) tools and through extensive pulsed RF signal measurements of a PA device under test. The measurement results confirm the validity of the proposed electrothermal analysis methods. The proposed methods show significantly faster simulation speed comparing to available CAD tools for electrothermal analysis. Moreover, the results reveal the importance of electrothermal characterization in the prediction of the temperature-aware PA dynamic operation

Advanced Modulation and Coding Technology Conference

The objectives, approach, and status of all current LeRC-sponsored industry contracts and university grants are presented. The following topics are covered: (1) the LeRC Space Communications Program, and Advanced Modulation and Coding Projects; (2) the status of four contracts for development of proof-of-concept modems; (3) modulation and coding work done under three university grants, two small business innovation research contracts, and two demonstration model hardware development contracts; and (4) technology needs and opportunities for future missions