26 research outputs found

    Digital Pre-distortion for Interference Reduction in Dynamic Spectrum Access Networks

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    Given the ever increasing reliance of today’s society on ubiquitous wireless access, the paradigm of dynamic spectrum access (DSA) as been proposed and implemented for utilizing the limited wireless spectrum more efficiently. Orthogonal frequency division multiplexing (OFDM) is growing in popularity for adoption into wireless services employing DSA frame- work, due to its high bandwidth efficiency and resiliency to multipath fading. While these advantages have been proven for many wireless applications, including LTE-Advanced and numerous IEEE wireless standards, one potential drawback of OFDM or its non-contiguous variant, NC-OFDM, is that it exhibits high peak-to-average power ratios (PAPR), which can induce in-band and out-of-band (OOB) distortions when the peaks of the waveform enter the compression region of the transmitter power amplifier (PA). Such OOB emissions can interfere with existing neighboring transmissions, and thereby severely deteriorate the reliability of the DSA network. A performance-enhancing digital pre-distortion (DPD) technique compensating for PA and in-phase/quadrature (I/Q) modulator distortions is proposed in this dissertation. Al- though substantial research efforts into designing DPD schemes have already been presented in the open literature, there still exists numerous opportunities to further improve upon the performance of OOB suppression for NC-OFDM transmission in the presence of RF front-end impairments. A set of orthogonal polynomial basis functions is proposed in this dissertation together with a simplified joint DPD structure. A performance analysis is presented to show that the OOB emissions is reduced to approximately 50 dBc with proposed algorithms employed during NC-OFDM transmission. Furthermore, a novel and intuitive DPD solution that can minimize the power regrowth at any pre-specified frequency in the spurious domain is proposed in this dissertation. Conventional DPD methods have been proven to be able to effectively reduce the OOB emissions that fall on top of adjacent channels. However more spectral emissions in more distant frequency ranges are generated by employing such DPD solutions, which are potentially in violation of the spurious emission limit. At the same time, the emissions in adjacent channel must be kept under the OOB limit. To the best of the author’s knowledge, there has not been extensive research conducted on this topic. Mathematical derivation procedures of the proposed algorithm are provided for both memoryless nonlinear model and memory-based nonlinear model. Simulation results show that the proposed method is able to provide a good balance of OOB emissions and emissions in the far out spurious domain, by reducing the spurious emissions by 4-5 dB while maintaining the adjacent channel leakage ratio (ACLR) improvement by at least 10 dB, comparing to the PA output spectrum without any DPD

    Augmented-LSTM and 1D-CNN-LSTM based DPD models for linearization of wideband power amplifiers

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    Abstract. Artificial Neural Networks (ANNs) have gained popularity in modeling the nonlinear behavior of wideband power amplifiers. Recently, modern researchers have used two types of neural network architectures, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to model power amplifier behavior and compensate for power amplifier distortion. Each architecture has its own advantages and limitations. In light of these, this study proposes two digital pre-distortion (DPD) models based on LSTM and CNN. The first proposed model is an augmented LSTM model, which effectively reduces distortion in wideband power amplifiers. The measurement results demonstrate that the proposed augmented LSTM model provides better linearization performance than existing state-of-the-art DPDs designed using ANNs. The second proposed model is a 1D-CNN-LSTM model that simplifies the augmented LSTM model by integrating a CNN layer before the LSTM layer. This integration reduces the number of input features to the LSTM layer, resulting in a low-complexity linearization for wideband PAs. The measurement results show that the 1D-CNN-LSTM model provides comparable results to the augmented LSTM model. In summary, this study proposes two novel DPD models based on LSTM and CNN, which effectively reduce distortion and provide low-complexity linearization for wideband PAs. The measurement results demonstrate that both models offer comparable performance to existing state-of-the-art DPDs designed using ANNs

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

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    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

    Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels

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    Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters

    Advanced digital predistortion of power amplifiers for mobile and wireless communications

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    This research work focuses on improving the performances of digital predistorters while maintaining low computational complexity for mobile and wireless communication systems. Initially, the thesis presents the fundamental theory of power amplifiers, overview of existing linearisation and memory-effects compensation techniques and reveals the current issues in the field. Further, the thesis depicts the proposed solutions to the problems, including the developed in-band distortion modelling technique, model extraction methods, memoryless digital predistortion technique based on distortion components iterative injection, baseband equalisation technique for minimising memory effects, Matlab-ADS co-simulation system and adaptation circuit with an offline training scheme. The thesis presents the following contributions of the research work. A generalized in-band distortion modelling technique for predicting the nonlinear behaviour of power amplifiers is developed and verified experimentally. Analytical formulae are derived for calculating predistorter parameters. Two model extraction techniques based on the least-squares regression method and frequency-response analysis are developed and verified experimentally. The area of implementation and the trade-off between the methods are discussed. Adjustable memoryless digital predistortion technique based on the distortion components iterative injection method is proposed in order to overcome the distortion compensation limit peculiar to the conventional injection techniques. A baseband equalisation method is developed in order to provide compensation of memory effects for increasing the linearising performance of the proposed predistorter. A combined Matlab-ADS co-simulation system is designed for providing powerful simulation tools. An adaptation circuit is developed for the proposed predistorter for enabling its adaptation to environmental conditions. The feasibility, performances and computational complexity of the proposed digital predistortion are examined by simulations and experimentally. The proposed method is tuneable for achieving the best ratio of linearisation degree to computational complexity for any particular application

    Advanced digital predistortion of power amplifiers for mobile and wireless communications

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    This research work focuses on improving the performances of digital predistorters while maintaining low computational complexity for mobile and wireless communication systems. Initially, the thesis presents the fundamental theory of power amplifiers, overview of existing linearisation and memory-effects compensation techniques and reveals the current issues in the field. Further, the thesis depicts the proposed solutions to the problems, including the developed in-band distortion modelling technique, model extraction methods, memoryless digital predistortion technique based on distortion components iterative injection, baseband equalisation technique for minimising memory effects, Matlab-ADS co-simulation system and adaptation circuit with an offline training scheme. The thesis presents the following contributions of the research work. A generalized in-band distortion modelling technique for predicting the nonlinear behaviour of power amplifiers is developed and verified experimentally. Analytical formulae are derived for calculating predistorter parameters. Two model extraction techniques based on the least-squares regression method and frequency-response analysis are developed and verified experimentally. The area of implementation and the trade-off between the methods are discussed. Adjustable memoryless digital predistortion technique based on the distortion components iterative injection method is proposed in order to overcome the distortion compensation limit peculiar to the conventional injection techniques. A baseband equalisation method is developed in order to provide compensation of memory effects for increasing the linearising performance of the proposed predistorter. A combined Matlab-ADS co-simulation system is designed for providing powerful simulation tools. An adaptation circuit is developed for the proposed predistorter for enabling its adaptation to environmental conditions. The feasibility, performances and computational complexity of the proposed digital predistortion are examined by simulations and experimentally. The proposed method is tuneable for achieving the best ratio of linearisation degree to computational complexity for any particular application.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Behavioral modeling techniques for power amplifier digital pre-distortion

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    Abstract. The dramatic increase in the capacity of telecommunication networks has increased the requirements of the devices, one example of which is the continuously widening bandwidth. Using broadband signals requires often high linearity from the transmitter — and especially from the power amplifier at the end of the transmitter chain — but at the same time, it should operate as efficiently as possible. The power amplifier is the most power consuming component in the transmitter and inherently nonlinear, so its linearization is an essential part of the overall system performance. Of the current linearization techniques, digital pre-distortion has established itself as the most common tool for providing better linearity and efficiency in a power amplifier and transmitter. In this thesis, the performance of power amplifier models used in digital pre-distortion was investigated and their differences compared to the more complex reference model used in the actual base station product. The aim of this thesis was to create a behavioral model that corresponds the physical component as accurately as the reference model. This behavioral model could be used for example to design and optimize a power amplifier and linearization algorithm without the need for the secret model for the product. This, in its turn, would result in more efficient work with third parties such as component vendors and reduce the linearization time. The performance parameters of the behavioral models were introduced at the beginning of the thesis. These were also used in later parts to analyse the measurement results. Power amplifier linearization measurements were performed under laboratory conditions on a MATLAB® test bench. From the results, it was found that the use of a simple memoryless behavioral model is not enough to describe the physical nonlinear component with sufficient accuracy. The results also showed that the complexity of the model reduces its accuracy if the model coefficients are not correctly positioned. In this thesis, we succeeded in creating a memory model that describes the reference model sufficiently accurately on several meters, taking into account also the memory effects of the power amplifier. This thesis thus provides a good basis for further development of the actual modeling tool for product projects.Tehovahvistimen käyttäytymistason mallinnustekniikat esisärötyksen tueksi. Tiivistelmä. Tietoliikenneverkkojen kapasiteetin räjähdysmäinen kasvu on lisännyt laitteiden vaatimuksia, joista yhtenä esimerkkinä on jatkuvasti suureneva kaistanleveys. Laajakaistaiset signaalit vaativat usein lähettimeltä — ja etenkin lähettimen loppupäässä olevalta tehovahvistimelta — korkeaa lineaarisuutta, mutta samalla sen on toimittava mahdollisimman tehokkaasti. Tehovahvistin on lähettimen eniten tehoa kuluttava komponentti ja luonnostaan epälineaarinen, joten sen linearisointi on oleellinen osa koko systeemin suorituskykyä. Nykyisistä linearisointitekniikoista digitaalinen esisärötys on vakiinnuttanut paikkansa yleisimpänä työkaluna paremman lineaarisuuden ja tehokkuuden saavuttamiseksi tehovahvistimessa. Tässä diplomityössä tutkittiin digitaalisessa esisärötyksessä käytettävien käyttäytymistason tehovahvistinmallien suorituskykyjä ja niiden eroja varsinaisessa tukiasematuotteessa käytettävään, monimutkaisempaan referenssimalliin verrattuna. Työn tavoitteena oli luoda käyttäytymismalli, jolla voidaan kuvata fyysistä komponenttia yhtä tarkasti kuin referenssimallilla. Mallia voitaisiin käyttää esimerkiksi uuden tehovahvistimen suunnittelussa ilman, että kaupalliseen tuotteeseen tulevaa, salaista referenssimallia on tarve käyttää. Näin voitaisiin tehostaa työskentelyä ulkopuolisten tahojen, kuten komponenttitoimittajien kanssa ja vähentää linearisointiin käytettävää aikaa. Työn alussa esiteltiin käyttäytymismallien suorituskykyparametrit, joita käytettiin mittaustulosten analysointiin. Tehovahvistimien linearisointimittaukset suoritettiin laboratorio-olosuhteissa MATLAB®-testipenkissä. Mittauksissa todettiin, että yksinkertainen, muistiton käyttäytymismalli ei riitä kuvaamaan fyysistä komponenttia riittävän tarkasti. Tuloksista pääteltiin myös, että liian kompleksinen malli heikentää sen tarkkuutta. Työssä onnistuttiin luomaan muistillinen käyttäytymismalli, joka kuvaa referenssimallia riittävän tarkasti usealla eri mittarilla tarkastellen — huomioiden osaltaan myös tehovahvistimen muistiefektejä. Tämä opinnäytetyö tarjoaa siis hyvän pohjan varsinaisen mallinnustyökalun jatkokehitykselle tuoteprojekteihin

    Digital Predistorion of 5G Millimeter-Wave Active Phased Arrays using Artificial Neural Networks

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    Energy-Efficient Distributed Estimation by Utilizing a Nonlinear Amplifier

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    abstract: Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.Dissertation/ThesisPh.D. Electrical Engineering 201
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