A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems

The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of-freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 100x10 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications.Comment: IEEE International Conference on Communications 201

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

Digital predistortion of RF amplifiers using baseband injection for mobile broadband communications

Radio frequency (RF) power amplifiers (PAs) represent the most challenging design parts of wireless transmitters. In order to be more energy efficient, PAs should operate in nonlinear region where they produce distortion that significantly degrades the quality of signal at transmitter’s output. With the aim of reducing this distortion and improve signal quality, digital predistortion (DPD) techniques are widely used. This work focuses on improving the performances of DPDs in modern, next-generation wireless transmitters. A new adaptive DPD based on an iterative injection approach is developed and experimentally verified using a 4G signal. The signal performances at transmitter output are notably improved, while the proposed DPD does not require large digital signal processing memory resources and computational complexity. Moreover, the injection-based DPD theory is extended to be applicable in concurrent dual-band wireless transmitters. A cross-modulation problem specific to concurrent dual-band transmitters is investigated in detail and novel DPD based on simultaneous injection of intermodulation and cross-modulation distortion products is proposed. In order to mitigate distortion compensation limit phenomena and memory effects in highly nonlinear RF PAs, this DPD is further extended and complete generalised DPD system for concurrent dual-band transmitters is developed. It is clearly proved in experiments that the proposed predistorter remarkably improves the in-band and out-of-band performances of both signals. Furthermore, it does not depend on frequency separation between frequency bands and has significantly lower complexity in comparison with previously reported concurrent dual-band DPDs

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

Experimental demonstration of digital predistortion for orthogonal frequency-division multiplexing-radio over fibre links near laser resonance

Radio over fibre (RoF), an enabling technology for distribution of wireless broadband service signals through analogue optical links, suffers from non-linear distortion. Digital predistortion has been demonstrated as an effective approach to overcome the RoF non-linearity. However, questions remain as to how the approach performs close to laser resonance, a region of significant dynamic non-linearity, and how resilient the approach is to changes in input signal and link operating conditions. In this work, the performance of a digital predistortion approach is studied for directly modulated orthogonal frequency-division multiplexing RoF links operating from 2.47 to 3.7 GHz. It extends previous works to higher frequencies, and to higher quadrature amplitude modulation (QAM) levels. In addition, the resilience of the predistortion approach to changes in modulation level of QAM schemes, and average power levels are investigated, and a novel predistortion training approach is proposed and demonstrated. Both memoryless and memory polynomial predistorter models, and a simple off-line least-squares-based identification method, are used, with excellent performance improvements demonstrated up to 3.0 GHz

Energy-Efficient Distributed Estimation by Utilizing a Nonlinear Amplifier

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

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

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

Transmitter Linearization for mm-Wave Communications Systems

There is an ever increasing need for enabling higher data rates in modern communication systems which brings new challenges in terms of the power consumption and nonlinearity of hardware components. These problems become prominent in power amplifiers (PAs) and can significantly degrade the performance of transmitters, and hence the overall communication system. Hence, it is of central importance to design efficient PAs with a linear operation region. This thesis proposes a methodology and a comprehensive framework to address this challenge. This is accomplished by application of predistortion to a mm-wave PA and an E-band IQ transmitter while investigating the trade-offs between linearity, efficiency and predistorter complexity using the proposed framework.In the first line of work, we have focused on a mm-wave PA. A PA has high efficiency at high input power at the expense of linearity, whereas it operates linearly for lower input power levels while sacrificing efficiency. To attain both linearity and efficiency, predistortion is often used to compensate for the PA nonlinearity. Yet, the trade-offs related to predistortion complexities are not fully understood. To address this challenge, we have used our proposed framework for evaluation of predistorters using modulated test signals and implemented it using digital predistortion and a mm-wave PA. This set-up enabled us to investigate the trade-offs between linearity, efficiency and predistorter complexity in a systematic manner. We have shown that to achieve similar linearity levels for different PA classes, predistorters with different complexities are needed and provided guidelines on the achievable limits in term linearity for a given predistorter complexity for different PA classes.In the second line of work, we have focused on linearization of an E-band transmitter using a baseband analog predistorter (APD) and under constraints given by a spectrum emission standard. In order to use the above proposed framework with these components, characterizations of the E-band transmitter and the APD are performed. In contrast to typical approaches in the literature, here joint mitigation of the PA and I/Q modulator impairments is used to model the transmitter. Using the developed models, optimal model parameters in terms of output power at the mask limit are determined. Using these as a starting point, we have iteratively optimized operating point of the APD and linearized the E-band transmitter. The experiments demonstrated that the analog predistorter can successfully increase the output power by 35% (1.3 dB) improvement while satisfying the spectrum emission mask