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

A proposal for the management of data driven services in smart manufacturing scenarios

205 p.This research work focuses on Industrial Big Data Services (IBDS) Providers, a specialization of ITServices Providers. IBDS Providers constitute a fundamental agent in Smart Manufacturing scenarios,given the wide spectrum of complex technological challenges involved in the adoption of the requireddata-related IT by manufacturers aiming at shifting their businesses towards Smart Manufacturing. Theoverarching goal of this research work is to provide contributions that (a) help the business sector ofIBDS Providers to manage their collaboration projects with manufacturing partners in order to deploy therequired data-driven services in Smart Manufacturing scenarios, and (b) adapt and extend existingconceptual, methodological, and technological proposals in order to include those practical elements thatfacilitate their use in business contexts. The main contributions of this dissertation focus on three specificchallenges related to the early stages of the data lifecycle, i.e. those stages that ensure the availability ofnew data to exploit, coming from monitored manufacturing facilities: (1) Devising a more efficient datastorage strategy that reduces the costs of the cloud infrastructure required by an IBDS Provider tocentralize and accumulate the massive-scale amounts of data from the supervised manufacturingfacilities; (2) Designing the required architecture for the data capturing and integration infrastructure thatsustains an IBDS Provider's platform; (3) The collaborative design process with partnering manufacturersof the required data-driven services for a specific manufacturing sector

Improving tribomechanical properties of polymeric nanocomposite coatings

Low friction, high wear resistance and strong adhesion in polymeric coatings employed in a variety of industrial and domestic processes such as in ball bearings, water repellent surfaces, antiadhesive coatings, and anticorrosion systems are of significant interest for energy saving and durability purposes. Even small increases in friction can have implications on energy efficiency, life time expectancy and performance of such coatings

Data redundancy reduction for energy-efficiency in wireless sensor networks: a comprehensive review

Wireless Sensor Networks (WSNs) play a significant role in providing an extraordinary infrastructure for monitoring environmental variations such as climate change, volcanoes, and other natural disasters. In a hostile environment, sensors' energy is one of the crucial concerns in collecting and analyzing accurate data. However, various environmental conditions, short-distance adjacent devices, and extreme usage of resources, i.e., battery power in WSNs, lead to a high possibility of redundant data. Accordingly, the reduction in redundant data is required for both resources and accurate information. In this context, this paper presents a comprehensive review of the existing energy-efficient data redundancy reduction schemes with their benefits and limitations for WSNs. The entire concept of data redundancy reduction is classified into three levels, which are node, cluster head, and sink. Additionally, this paper highlights existing key issues and challenges and suggested future work in reducing data redundancy for future research

A Time Series Dimensionality Reduction Method for Maximum Deviation Reduction and Similarity Search

Similarity search over time series is essential in many applications. However, it may cause a “dimensionality curse” due to the high dimensionality of time series. Various dimensionality reduction methods have been developed. Some of them sacrifice max deviation to get a faster dimensionality reduction, such as equal-length segment dimensionality reduction methods: Piecewise Linear Approximation (PLA), Piecewise Aggregate Approximation (PAA), Chebyshev Polynomials (CHEBY), Piecewise Aggregate Approximation Lagrangian Multipliers (PAALM) and Symbolic Aggregate Approximation (SAX). Some sacrifice dimensionality reduction time for the smallest max deviation, such as adaptive-length segment dimensionality reduction methods: Adaptive Piecewise Linear Approximation (APLA) and Adaptive Piecewise Constant Approximation (APCA). APLA uses a guaranteed upper bound for the best max deviation with slow dimensionality reduction time. We investigate the existing basic dimensionality reduction techniques for time series data. We point out the limitations of the existing dimensionality reduction techniques and evaluate the dimensionality reduction techniques on real-life datasets. We also conduct preliminary research and make some improvements to the existing dimensionality reduction techniques. Our experimental results on several datasets compare and verify the efficiency and effectiveness of the existing techniques, including several dimensionality reduction techniques, one index-building method, and two k-Nearest Neighbours (k-NN) search methods. We propose an adaptive-length segment dimensionality reduction method called Self Adaptive Piecewise Linear Approximation (SAPLA). It consists of 1) initialization, 2) split & merge iteration, and 3) segment endpoint movement iteration. Increment area, reconstruction area, conditional upper bound and several equations are applied to prune redundant computations. The experiments show this method can speed up about n (time series length) times than APLA when reducing the dimensionality of the original time series with minor max deviation loss. We propose a lower bound distance measure between two time series to guarantee no false dismissals and tightness for adaptive-length segment dimensionality reduction methods. When mapping time series into a tree index, we propose Distance Based Covering with Convex Hull Tree (DBCH-tree) and split node and pick branch by using the proposed lower bounding distance, not waste area. Our experiments show that DBCH-tree helps improve k-NN search over time series using dimensionality reduction methods