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

An LMS-based adaptive predistorter for cancelling nonlinear memory effects in RF power amplifiers

This paper presents the design of an adaptive Digital Predistorter (DPD) for Power Amplifier (PA) linearization whoseimplementation and real time adaptation can be fully performed in a Field Programmable Gate Array (FPGA). The distinctive characteristic of this adaptive DPD is its straightforward deduction from a Nonlinear Auto Regressive Moving Average (NARMA) PA model and the possibility to be completely implemented in a FPGA without the need of an additional digital signal processor performing the DPD adaptation. The adaptive DPD presents a NARMA structure that can be implemented by means of Look-Up Tables (LUTs). This configuration results in a Multi-LUT implementation where LUT contents are directly updated by means of an LMS algorithm. Details on the internal adaptive DPD organization as well as its linearization capabilities are provided, taking into account memory effects compensation

Companding and Predistortion Techniques for Improved Efficiency and Performance in SWIPT

In this work, we analyze how the use of companding techniques, together with digital predistortion (DPD), can be leveraged to improve system efficiency and performance in simultaneous wireless information and power transfer (SWIPT) systems based on power splitting. By taking advantage of the benefits of each of these well-known techniques to mitigate non-linear effects due to power amplifier (PA) and energy harvesting (EH) operation, we illustrate how DPD and companding can be effectively combined to improve the EH efficiency while keeping unalterable the information transfer performance. We establish design criteria that allow the PA to operate in a higher efficiency region so that the reduction in peak-to-average power ratio over the transmitted signal is translated into an increase in the average radiated power and EH efficiency. The performance of DPD and companding techniques is evaluated in a number of scenarios, showing that a combination of both techniques allows to significantly increase the power transfer efficiency in SWIPT systems.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Adaptive Fuzzy Logic System for the Compensation of Nonlinear Distortion in Wireless Power Amplifiers

Computational intelligent systems are becoming an increasingly attractive solution for power amplifier (PA) behavioural modelling, due to their excellent approximation capability. This paper utilizes an adaptive fuzzy logic system (AFLS) for the modelling of the highly nonlinear MIMIX CFH2162-P3 PA. Moreover, PA’s inverse model based also on AFLS has been developed in order to act as a pre-distorter unit. Driving an LTE 1.4 MHz 64 QAM signal at 880 MHz as centre frequency at PA’s input, very good modelling performance was achieved, for both PA’s forward and inverse dynamics. A comparative study of AFLS and neural networks (NN) has been carried out to establish AFLS as an effective, robust, and easy-to-implement baseband model, which is suitable for inverse modelling of PAs and capable to be used as an effective digital pre-distorter. Pre-distortion system based on AFLS, achieved distortion suppression of 84.2%, compared to the 48.4% gained using the NN-based equivalent schem

Design and Implementation of a Software Predistorter for amplifier linearization in OFDM-based SDR systems

In modern wireless communication systems, an important role is played by the amplifier in the RF transmitter. It controls the maximum distance covered, the battery consumption for mobile devices, heating, etc. Nowadays RF transmitter has a lot of uses, starting from old FM stations, and arriving, in the recent period, to piloting of drones. Simplifying as much as possible, what this device accomplishes is to convert the baseband signal containing the data to be transmitted into a radio frequency signal able to travel through the ether. This can be done directly, or in two distinct phases before passing to an intermediate frequency (IF). In both cases, the signal after conversion must be amplified with a power amplifier and then transmitted on the channel. This thesis will focus on the amplifier part of the transmitter. In particular, existing predistortion techniques, used to improve the linearity of the power amplifier, and a software, non-real time, predistorter developed for the thesis will be described

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

Dynamic nonlinear behavioral modeling and adaptive predistortion for RF transmitters

Motivation -- Nonlinear dynamic behaviour, two-and three-box models -- Objectives and outline of the thesis -- Two-Box models, de-embedding nonlinearities and dynamic memory effects -- Transmitter prototype -- Hammerstein and Wiener model construction -- Three-box oriented nonlinear model -- Three-box model's two-stage identification procedure -- Adaptive predistortion construction using single tone signal -- Hypothetical model and adaptive predistortion -- Construction of the complete predistorted system with a two-box model -- Complete predistorted system and linearization validation with CDMA signal

Analyse et développement de radar à diversité spatiale: applications à l'évitement de collisions de véhicules et au positionnement local

RÉSUMÉ En tant que dispositif d’assistance à la conduite sécuritaire de la prochaine génération d’automobiles, les radars ont suscité beaucoup d’intérêt auprès des chercheurs du domaine au cours de la dernière décennie. Désormais appelés les radars à évitement/avertissement des collisions (collision avoidance/warning), ces radars ouvrent leurs chemins pour venir en aide aux conducteurs dans les conditions climatiques difficiles ou en perte de concentration. Une autre application récente des radars est dans les systèmes de positionnement local. Dans les milieux industriels et médicaux, nous avons besoin de localiser les équipements sollicités fréquemment dont les contraintes de coût et d’encombrement limitent le nombre d’exemplaires. Par ailleurs, avec le vieillissement de la population et les besoins croissants des personnes âgées en soins médicaux, la nécessité d’un système permettant l’évaluation à distance de la position (debout, couché, tombé, …) des patients se fait sentir depuis un certain temps. Un autre exemple est la surveillance des enfants et des personnes à risque dans un endroit peuplé comme dans une foire ou sur une plage. La possibilité de pouvoir repérer les pompiers dans un immeuble en feu serait une autre application intéressante. Devant la multitude de ces applications potentielles et attrayantes dont les contextes évoluent, le système de positionnement local doit à son tour évoluer et s’adapter. Par ailleurs, il est bien connu que les radars, comme tous les systèmes de télécommunications sans fil, sont confrontés au problème d’évanouissement du signal. D’une manière générale, ce problème est dû aux propagations multi-chemins du signal. Autrement dit, les réflexions multiples du signal par les objets environnants mobiles et stationnaires se neutralisent de façon aléatoire au point d’arrivée où se trouve l’antenne réceptrice. Dans un contexte différent et pour des raisons à priori différentes, les radars subissent le même type de défaillances. Même dans un milieu dégagé et avec la visibilité directe (line of sight) sur la cible, les radars sont exposés au problème d’évanouissement du signal (power fading) dû aux changements de la surface équivalente radar (radar cross section) de la cible.----------ABSTRACT As a device enabling the safe driving of the next generation of vehicles, the radars have trigged much interest among the researchers of this field in the last decade. Recently called collision avoidance/warning radar, this type of radar can assist drivers in bad weather conditions and when driver’s concentration and attention fails. In the other hand, the utilization context of systems has evolved and will go even further in the upcoming years. In the industrial locations and medical centers, we need to locate most requested equipments. With aging society and the growing needs of elder people for medical care, a system capable of remotely sensing the patients (standing, lying down or falling) has been studied since the beginning of the new century. Other interesting example would include the surveillance of children in crowded places (beaches or amusement parks) or locating fire fighters in a building. In a context of evolving applications, the vertical local positioning system should also evolve. The most frequently used method in local positioning systems is to make use of three base stations at different places and to measure the range of the tag by each base station. Then the exact location of the tag is calculated by triangulation. In practice, a fourth base station is added for more reliability and time synchronization. In some situations like the surveillance of a beach or a building on fire, installing the third base station would be a difficult or time consuming task. Our idea is to elevate the third base station at a reasonable height. This will provide a better signal quality and more information about the target can be obtained. It is a new type of local positioning system that we call VLPS (Vertical Local Positioning System). We will examine the constraints of VLPS in the second part of this thesis. Moreover, it is well known that the radars, as well as all wireless telecommunication systems, are confronted with the problem of fading signals. Generally, this problem is due to multi-path effects of signal propagations. In other words, the multiple signal reflections by the surrounding stationary and mobile objects are randomly neutralized at the arriving point of the receiving antenna. In a different context and for apparently unlike motives, the radars are subject to the same issue. Even when the target is in the line-up site of transmitting and receiving antennas (radars), they face the same type of scintillations due to the variation of the radar cross section (RCS) of a target. Indeed, the radar cross section of the majority of targets strongly depends on the aspect angles of the receiving and transmitting antennas

Tanlock based loop with improved performance

This thesis is focused on the design, analysis, simulation and implementation of new improved architectures of the Time Delay Digital Tanlock Loop (TDTL) based digital phase-locked loop (DPLL). The proposed architectures overcome some fundamental limitations exhibited by the original TDTL. These limitations include the presence of nonlinearity in the phase detector (PD), the non-zero phase error of the first-order loop, the restricted locking range, particularly of the second-order loop, the limited acquisition speed and the noise performance. Two approaches were adopted in this work to alleviate these limitations: the first involved modifying the original TDTL through the incorporation of auxiliary circuit blocks that enhance its performance, whilst the second involved designing new tanlock-based architectures. The proposed architectures, which resulted from the above approaches, were tested under various input signal conditions and their performance was compared with the original TDTL. The proposed architectures demonstrated an improvement of up to fourfold in terms of the acquisition times, twofold in noise performance and a marked enhancement in the linearity and in the locking range. The effectiveness of the proposed tanlock-based architectures was also assessed and demonstrated by using them in various applications, which included FM demodulation, FM threshold extension, FM demodulation with improved THD (total harmonic distortion), and Doppler effect improvement. The results from these applications showed that the performance of the new architectures outperformed the original TDTL. Real-time performance of these architectures was evaluated through implementation of some of them on an FPGA (field-programmable gate array) based system. Practical results from the prototype FPGA based implementations confirmed the simulation results obtained from MATLAB/Simulink

Intégration de Réseaux de Neurones pour la Télémétrie Laser

Grandes lignes : Un réseau de neurones est une architecture paramétrable composée de plusieurs modules appelés neurones. Ils peuvent être utilisés pour compenser des variations non souhaitées de certains phénomènes physiques ou pour effectuer des tâches de discrimination. Un réseau de neurones a été intégré en technologie CMOS basse tension pour être implanté au sein d'un télémètre laser par déphasage. Deux études ont été menées en parallèle. La première consiste à lever l'indétermination sur la mesure de distance déduite de la mesure de déphasage. La seconde étude permet la classification de différents types de surfaces à partir de deux signaux issus du télémètre. Résumé détaillé : Un réseau de neurones a la faculté de pouvoir être entraîné afin d'accomplir une tâche d'approximation de fonction ou de classification à partir d'un nombre limité de données sur un intervalle bien défini. L'objectif de cette thèse est de montrer l'intérêt d'adapter les réseaux de neurones à un type de système optoélectronique de mesure de distance, la télémétrie laser par déphasage. La première partie de ce manuscrit développe de manière succincte leurs diverses propriétés et aptitudes, en particulier leur reconfigurabilité par l'intermédiaire de leurs paramètres et leur capacité à être intégré directement au sein de l'application. La technique de mesure par télémétrie laser par déphasage est développée dans le deuxième chapitre et comparée à d'autres techniques télémétriques. Le troisième chapitre montre qu'un réseau de neurones permet d'améliorer nettement le fonctionnement du télémètre. Une première étude met en valeur sa capacité à accroître la plage de mesure de distance sans modifier la résolution. Elle est réalisée à partir de mesures expérimentales afin de prouver le réel intérêt de la méthode comportementale développée. La deuxième étude ouvre une nouvelle perspective relative à l'utilisation d'un télémètre laser par déphasage, celle d'effectuer la classification de différents types de surfaces sur des plages de distances et d'angles d'incidence variables. Pour valider expérimentalement ces deux études, les cellules de base du neurone de type perceptron multi-couches ont été simulées puis implantées de manière analogique. Les phases de simulation, de conception et de test du neurone analogique sont détaillées dans le quatrième chapitre. Un démonstrateur du réseau de neurones global a été réalisé à partir de neurones élémentaires intégrés mis en parallèle. Une étude de la conception des mêmes cellules en numérique est détaillée succinctement dans le cinquième chapitre afin de justifier les avantages associés à chaque type d'intégration. Le dernier chapitre présente les phases d'entraînement et de validation expérimentales du réseau intégré pour les deux applications souhaitées. Ces phases de calibrage sont effectuées extérieurement à l'ASIC, par l'intermédiaire de l'équation de transfert déterminée après caractérisation expérimentale et qualification du réseau de neurones global. Les résultats expérimentaux issus de la première étude montrent qu'il est possible d'obtenir à partir des signaux de sorties du télémètre et du réseau de neurones, une mesure de distance de précision (50µm) sur un intervalle de mesure 3 fois plus important que celui limité à la mesure du déphasage. Concernant l'application de discrimination de surfaces, le réseau de neurones analogique implanté est capable de classer quatre types de cibles sur l'intervalle [0.5m ; 1.25m] pour un angle d'incidence pouvant varier de - π /6 à + π /6. ABSTRACT : Outline : A neural network is a trainable structure composed by modules called neurons. They may be used in order to compensate adverse variations of physical phenomenon or to achieve discrimination tasks. Two studies were held in order to integrate a neural network in low voltage CMOS technology in a phase-shift laser rangefinder. The first one consists in raising the indecision on distance measurement deduced from the phase-shift measurement. The aim of the second study is to classify different kinds of surfaces using two signals issued from the rangefinder. Detailed abstract : A neural network has the capability to be trained in order to approximate functions or to achieve classification from a limited number of data on a well defined interval. The first part of the manuscript develops succinctly their various properties and aptitudes, particularly their reconfigurability through they parameters and their capability to be integrated directly in the application. The aim of this thesis is to demonstrate the interest of adapting neural networks to a type of distance measurement optoelectronic system, the phase-shift laser rangefinding. This measurement technique is developed in the second chapter and compared to other rangefinding techniques. The third chapter demonstrates that a neural network allows to improve considerably the rangefinder functioning. A first study highlights its capability to increase the distance measurement range without modifying the resolution. It is achieved from experimental measurements, in order to prove the real interest of the developed behavioural method. In a second study, the same neural network structure is used in order to show its capability to discriminate different types of surfaces on variable distance and incidence angle ranges. The main cells of the multi-layer perceptron-type neuron were simulated then implanted in analog. A conception study of the same cells in digital were achieved in order to justify the advantages associated to each type of integration. The simulation, conception and test stages are detailed in the fourth chapter. The whole neural network were achieved from elementary integrated neurons in parallel. The digital version of the neuron is succinctly detailed then compared to the analog structure in the fifth chapter. The last part of the thesis presents the behavioural and test training and validation phases of the integrated network for the two developed applications. These calibrage phases are achieved off-chip through the transfer equation issued from experimental characterisation and qualification of the whole neural network. Thus, by combining the signals provided by the phasemeter and the neural network outputs, it is possible to reach a distance measurement with high resolution (50µm) on a measurement range three times wider than the one limited by the phase-shift measurement. Concerning the surfaces discrimination application, the implanted analog neural network is capable of classifying four types of targets on the interval [0.5m ; 1.25m] for a incidence angle varying between - π /6 and + π /