RF Power Amplifier Linearization in Professional Mobile Radio Communications Using Artificial Neural Networks

This paper is focused on the linearization of the radio frequency power amplifier of a professional digital handheld by means of an artificial neural network. The simplicity of the neural network that is used, together with the fact that a feedback path is unnecessary, makes this solution ideal to reduce both the cost of a handheld and its hardware complexity, while fully maintaining its performance. A compensation system is also needed to keep the linearization characteristics of the neural network stable against frequency, temperature, and voltage variations. The whole solution that comprises both the neural network and the compensation system has been implemented in the digital signal processor of a real handheld and afterward fully tested. It has proved to be satisfactory to meet the telecommunication standard requirements in all frequency, temperature, and voltage ranges under consideration while efficient to lower the computational cost of the handheld and to make its internal hardware simpler in comparison with other traditional linearization techniques. The results obtained demonstrate that a neural network can be used to linearize the power amplifiers that are used in transmitters of telecommunication equipment, leading to a significant reduction of both their hardware cost and complexity

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

Joint compensation of I/Q impairments and PA nonlinearity in mobile broadband wireless transmitters

The main focus of this thesis is to develop and investigate a new possible solution for compensation of in-phase/quadrature-phase (I/Q) impairments and power amplifier (PA) nonlinearity in wireless transmitters using accurate, low complexity digital predistortion (DPD) technique. After analysing the distortion created by I/Q modulators and PAs together with nonlinear crosstalk effects in multi-branch multiple input multiple output (MIMO) wireless transmitters, a novel two-box model is proposed for eliminating those effects. The model is realised by implementing two phases which provide an optimisation of the identification of any system. Another improvement is the capability of higher performance of the system without increasing the computational complexity. Compared with conventional and recently proposed models, the approach developed in this thesis shows promising results in the linearisation of wireless transmitters. Furthermore, the two-box model is extended for concurrent dual-band wireless transmitters and it takes into account cross-modulation (CM) products. Besides, it uses independent processing blocks for both frequency bands and reduces the sampling rate requirements of converters (digital-to-analogue and analogue-to-digital). By using two phases for the implementation, the model enables a scaling down of the nonlinear order and the memory depth of the applied mathematical functions. This leads to a reduced computational complexity in comparison with recently developed models. The thesis provides experimental verification of the two-box model for multi-branch MIMO and concurrent dual-band wireless transmitters. Accordingly, the results ensure both the compensation of distortion and the performance evaluation of modern broadband wireless transmitters in terms of accuracy and complexity

Estimation of lower extremity joint moments in Clinical Gait Analysis by using Artificial Neural Networks

Gait analysis is typically conducted using an optoelectronic system which is known as the standard method for motion analysis. Despite advance development of instruments related to the optoelectronic approach, there are still a few limitations of the traditional gait analysis which limit the accessibility for individuals who would benefit from the investigation. A newly developed three-dimension motion capture system, known as Inertial Measurement Units (IMU) was introduced as an option for gait analysis. The IMU system is a transportable camera-free motion capture system. This also motivated the principle of out-of-the lab gait analysis. To broaden the use of the new system, this PhD project was conducted to examine whether the system should be used confidently for clinical gait analysis. The main purpose of this PhD project was to examine the feasibility of incorporating a machine learning method to estimate the kinetics of gait using the kinematics data obtained from an IMU system. Firstly, as pilot studies, an artificial neural network (ANN) was trained using gait data derived from the potential input signals which were signals of marker coordinates and joint angles obtained from an IMU system (Xsens) to predict joint moments of lower extremities. Promising findings were found as the ANN could reasonably predict the target joint moments. The results also showed the generalisation ability of the ANN to estimate the joint moment that it has not seen before, for instance, the ANN could fairly predict joint moments of the contralateral limb. The Xsens system was validated against the standard motion capture system before the main estimation study of the joint moment in gait began. The results revealed that joint angles obtained from the Xsens were comparable with the optoelectronic system in the sagittal plane and less comparable in the frontal plane according to the coefficient of multiple correlation and the linear fit methods. The results from the transverse plane were non-real numbers. The ANN was then trained using the joint angles derived from the Xsens system of three different walking speeds to predict the knee abduction moment (KAM). Gait data of 15 healthy volunteers were used to train the network. The ANN performed well, shown by small values of average normalised root mean square errors. Several methods were used to enhance the ANN performance. Due to the limited number of gait data used to train the network the randomisation of the input-target output data was performed. The results showed a remarkable improvement of the ANN performance. The best KAM estimation was found when the data of marker coordinates were used to train the ANN instead of joint angles. As few as three marker coordinates could provide sufficient information for the ANN to be trained and predict the KAM accurately. Principal component analysis was also used as input data manipulation and provided a reasonable KAM prediction. Overall, the kinematic gait data obtained from the Xsens could be used to train the ANN to predict the KAM in healthy gait. There is a possibility to combine machine learning methods with IMU data to produce a clinical gait analysis without the restriction of the traditional motion laboratory

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 /

Levenberg-Marquardt learning neural network for adaptive predistortion for time-varying HPA with memory in OFDM systems

International audienceThis paper presents a new adaptive pre-distortion (PD) technique, based on neural networks (NN) with tap delay line for linearization of High Power Amplifier (HPA) exhibiting memory effects. The adaptation, based on iterative algorithm, is derived from direct learning for the NN PD. Equally important, the paper puts forward the studies concerning the application of different NN learning algorithms in order to determine the most adequate for this NN PD. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, is based on some quality measure (Mean Square Error), the required training time to reach a particular quality level and computation complexity. The chosen adaptive pre-distortion (NN structure associated with an adaptive algorithm) have a low complexity, fast convergence and best performance