Energy-driven techniques for massive machine-type communications

In the last few years, a lot of effort has been put into the development of the fifth generation of cellular networks (5G). Given the vast heterogeneity of devices coexisting in these networks, new approaches have been sought to meet all requirements (e.g., data rate, coverage, delay, etc.). Within that framework, massive machine-type communications (mMTC) emerge as a promising candidate to enable many Internet of Things applications. mMTC define a type of systems where large sets of simple and battery-constrained devices transmit short data packets simultaneously. Unlike other 5G use cases, in mMTC, a low cost and power consumption are extensively pursued. Due to these specifications, typical humantype communications (HTC) solutions fail in providing a good service. In this dissertation, we focus on the design of energy-driven techniques for extending the lifetime of mMTC terminals. Both uplink (UL) and downlink (DL) stages are addressed, with special attention to the traffic models and spatial distribution of the devices. More specifically, we analyze a setup where groups of randomly deployed sensors send their (possibly correlated) observations to a collector node using different multiple access schemes. Depending on their activity, information might be transmitted either on a regular or sporadic basis. In that sense, we explore resource allocation, data compression, and device selection strategies to reduce the energy consumption in the UL. To further improve the system performance, we also study medium access control protocols and interference management techniques that take into account the large connectivity in these networks. On the contrary, in the DL, we concentrate on the support of wireless powered networks through different types of energy supply mechanisms, for which proper transmission schemes are derived. Additionally, for a better representation of current 5G deployments, the presence of HTC terminals is also included. Finally, to evaluate our proposals, we present several numerical simulations following standard guidelines. In line with that, we also compare our approaches with state-of-the-art solutions. Overall, results show that the power consumption in the UL can be reduced with still good performance and that the battery lifetimes can be improved thanks to the DL strategies.En els últims anys, s'han dedicat molts esforços al desenvolupament de la cinquena generació de telefonia mòbil (5G). Donada la gran heterogeneïtat de dispositius coexistint en aquestes xarxes, s'han buscat nous mètodes per satisfer tots els requisits (velocitat de dades, cobertura, retard, etc.). En aquest marc, les massive machine-type communications (mMTC) sorgeixen com a candidates prometedores per fer possible moltes aplicacions del Internet of Things. Les mMTC defineixen un tipus de sistemes en els quals grans conjunts de dispositius senzills i amb poca bateria, transmeten simultàniament paquets de dades curts. A diferència d'altres casos d'ús del 5G, en mMTC es persegueix un cost i un consum d'energia baixos. A causa d'aquestes especificacions, les solucions típiques de les human-type communications (HTC) no aconsegueixen proporcionar un bon servei. En aquesta tesi, ens centrem en el disseny de tècniques basades en l'energia per allargar la vida útil dels terminals mMTC. S'aborden tant les etapes del uplink (UL) com les del downlink (DL), amb especial atenció als models de trànsit i a la distribució espacial dels dispositius. Més concretament, analitzem un escenari en el qual grups de sensors desplegats aleatòriament, envien les seves observacions (possiblement correlades) a un node col·lector utilitzant diferents esquemes d'accés múltiple. Depenent de la seva activitat, la informació es pot transmetre de manera regular o esporàdica. En aquest sentit, explorem estratègies d'assignació de recursos, compressió de dades, i selecció de dispositius per reduir el consum d'energia en el UL. Per millorar encara més el rendiment del sistema, també estudiem protocols de control d'accés al medi i tècniques de gestió d'interferències que tinguin en compte la gran connectivitat d'aquestes xarxes. Per contra, en el DL, ens centrem en el suport de les wireless powered networks mitjançant diferents mecanismes de subministrament d'energia, per als quals es deriven esquemes de transmissió adequats. A més, per una millor representació dels desplegaments 5G actuals, també s'inclou la presència de terminals HTC. Finalment, per avaluar les nostres propostes, presentem diverses simulacions numèriques seguint pautes estandarditzades. En aquesta línia, també comparem els nostres enfocaments amb les solucions de l'estat de l'art. En general, els resultats mostren que el consum d'energia en el UL pot reduir-se amb un bon rendiment i que la durada de la bateria pot millorar-se gràcies a les estratègies del DL.En los últimos años, se han dedicado muchos esfuerzos al desarrollo de la quinta generación de telefonía móvil (5G). Dada la gran heterogeneidad de dispositivos coexistiendo en estas redes, se han buscado nuevos métodos para satisfacer todos los requisitos (velocidad de datos, cobertura, retardo, etc.). En este marco, las massive machine-type communications (mMTC) surgen como candidatas prometedoras para hacer posible muchas aplicaciones del Internet of Things. Las mMTC definen un tipo de sistemas en los cuales grandes conjuntos de dispositivos sencillos y con poca batería, transmiten simultáneamente paquetes de datos cortos. A diferencia de otros casos de uso del 5G, en mMTC se persigue un coste y un consumo de energía bajos. A causa de estas especificaciones, las soluciones típicas de las human-type communications (HTC) no consiguen proporcionar un buen servicio. En esta tesis, nos centramos en el diseño de técnicas basadas en la energía para alargar la vida ´útil de los terminales mMTC. Se abordan tanto las etapas del uplink (UL) como las del downlink (DL), con especial atención a los modelos de tráfico y a la distribución espacial de los dispositivos. Más concretamente, analizamos un escenario en el cual grupos de sensores desplegados aleatoriamente, envían sus observaciones (posiblemente correladas) a un nodo colector utilizando diferentes esquemas de acceso múltiple. Dependiendo de su actividad, la información se puede transmitir de manera regular o esporádica. En este sentido, exploramos estrategias de asignación de recursos, compresión de datos, y selección de dispositivos para reducir el consumo de energía en el UL. Para mejorar todavía más el rendimiento del sistema, también estudiamos protocolos de control de acceso al medio y técnicas de gestión de interferencias que tengan en cuenta la gran conectividad de estas redes. Por el contrario, en el DL, nos centramos en el soporte de las wireless powered networks mediante diferentes mecanismos de suministro de energía, para los cuales se derivan esquemas de transmisión adecuados. Además, para una mejor representación de los despliegues 5G actuales, también se incluye la presencia de terminales HTC. Finalmente, para evaluar nuestras propuestas, presentamos varias simulaciones numéricas siguiendo pautas estandarizadas. En esta línea, también comparamos nuestros enfoques con las soluciones del estado del arte. En general, los resultados muestran que el consumo de energía en el UL puede reducirse con un buen rendimiento y que la duración de la batería puede mejorarse gracias a las estrategias del DLPostprint (published version

Approximations of the aggregated interference statistics for outage analysis in massive MTC

This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type-communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram–Charlier series expansion of a truncated Gaussian kernel is proposed. We use this approximation to derive an analytic closed-form expression for the outage probability, corresponding to the event of the signal-to-interference-and-noise ratio being below a detection threshold. This metric is useful since it can be used for evaluating the performance of mMTC systems. We analyze, as an illustrative application of the previous approximation, a scenario with several multi-antenna collector nodes, each equipped with a set of predefined spatial beams. We consider two setups, namely single- and multiple-resource, in reference to the number of resources that are allocated to each beam. A graph-based approach that minimizes the average outage probability, and that is based on the statistics approximation, is used as allocation strategy. Finally, we describe an access protocol where the resource identifiers are broadcast (distributed) through the beams. Numerical simulations prove the accuracy of the approximations and the benefits of the allocation strategy.Peer ReviewedPostprint (published version

Pilot contamination reduction in massive MIMO systems

Massive MIMO systems have been pointed out as one of the possible strategies to enhance system performance and reach the high data rates modern wireless communications demand. It represents a breakthrough in modern investigations given the new degrees of freedom and the extra dimensional space it provides. However, given the lack of channel knowledge, estimation must be employed. After this process, some interference due to the fast variation of the channel, which implies users sharing training sequences, is left. This is commonly referred to as pilot contamination and heavily compromises the throughput, specially in the large scale antennas regime. In this thesis, we will first study in detail this dramatic effect for later introducing different proposals that attempt to reduce its impact. In particular, we will start with the use of two main filters as basic processing. Next, allocation schemes to properly distribute users are discussed. Then, we will suggest projection based methods that transform the estimates with the purpose of canceling the undesired portions and strengthen the user destined signals. At the end of this analysis, it is shown numerically that the approaches presented behave well in these scenarios and help mitigate the interference created. This allows a faster and more robust transmission of information to take place in environments where pilot contamination is present

Sensor selection and distributed quantization for energy efficiency in massive MTC

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy.The work presented in this paper has been carried out within the framework of the project ROUTE56 (PID2019-104945GB-I00/ AEI/10.13039/501100011033) funded by the Agencia Estatal de Investigación (Spanish Ministry of Science and Innovation); the FPI grant BES-2017-079994, funded by the Spanish Ministry of Science, Innovation and Universities; and the grant 2017 SGR 578, funded by the Catalan Government (AGAUR, Departament de Rercerca i Universitats, Generalitat de Catalunya).Peer ReviewedPostprint (author's final draft

Ship detection in SAR images based on Maxtree representation and graph signal processing

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public.Peer ReviewedPostprint (author's final draft

SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring

Este proyecto es un trabajo de investigación exploratorio sobre el uso de representación max-tree para imágenes radar (SAR). El potencial de este enfoque se va a estudiar en el contexto de la detección y el seguimiento de barcos con imágenes adquiridas desde satélites SAR.Nowadays, object detection is a recurrent and important field of study in Image Processing and particularly in Remote Sensing. In fact, it has evolved into a necessary application for our society as many technologies are based on it. Thus, the purpose of this project is to detect a specific type of objects: ships in SAR (Synthetic Aperture Radar) imagery. To do so, the max-tree representation together with image processing and classification techniques are employed. More precisely, search techniques are applied to the max-trees in order to obtain the node (i.e. unitary element of such structure) that best fits the ship(s). Geometrical and Statistical descriptors are defined as well as their evolution along the various branches of the tree so that an efficient and robust detection can be done. Moreover, it has been observed that the performance of the whole system has led to satisfactory results (in terms of Precision and Recall).Actualmente, la detección de objetos es un campo de estudio recurrente i importante dentro del procesamiento de imagen y particularmente en teledetección. De hecho, ha evolucionado hacia una aplicación necesaria para nuestra sociedad ya que muchas tecnologías se basan en ella. Por lo tanto, el propósito de este proyecto es detectar un tipo específico de objetos: barcos en imaginería SAR (Synthetic Aperture Radar). Para lograrlo, se utiliza la representación max-tree juntamente con procesamiento de imagen y técnicas de clasificación. Además, se aplican técnicas de búsqueda a los max-trees para obtener el nodo (elemento unitario de dicha estructura) que mejor aproxima los barcos. Se definen descriptores geométricos y estadísticos así como su evolución a lo largo de las numerosas ramas del árbol de manera que se pueda realizar una detección eficiente y robusta. Asimismo, se ha observado la funcionalidad del sistema ha conducido a resultados satisfactorios (en términos de Precision and Recall).Actualment, la detecció d’objectes és un camp d’estudi recurrent i important dins del processament d’imatge i particularment en teledetecció. De fet, ha evolucionat cap a una aplicació necessària per a la nostra societat ja que moltes tecnologies es basen en ella. Per tant, el propòsit d’aquest projecte és detectar un tipus específic d’objectes: vaixells en imatgeria SAR (Synthetic Aperture Radar). Per aconseguir-ho, s’empra la representació max-tree juntament amb processament d’imatge i tècniques de classificació. A més, s’apliquen tècniques de recerca als max-trees per tal d’obtenir el node (element unitari d’aquesta estructura) que millor aproxima els vaixells. Es defineixen descriptors geomètrics i estadístics així com la seva evolució al llarg de les nombroses branques de l’arbre de manera que es puguin realitzar una detecció eficient i robusta. També, s’ha observat que la funcionalitat de tot el sistema ha conduït a resultats satisfactoris (en termes de Precision and Recall)

SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring

Este proyecto es un trabajo de investigación exploratorio sobre el uso de representación max-tree para imágenes radar (SAR). El potencial de este enfoque se va a estudiar en el contexto de la detección y el seguimiento de barcos con imágenes adquiridas desde satélites SAR.Nowadays, object detection is a recurrent and important field of study in Image Processing and particularly in Remote Sensing. In fact, it has evolved into a necessary application for our society as many technologies are based on it. Thus, the purpose of this project is to detect a specific type of objects: ships in SAR (Synthetic Aperture Radar) imagery. To do so, the max-tree representation together with image processing and classification techniques are employed. More precisely, search techniques are applied to the max-trees in order to obtain the node (i.e. unitary element of such structure) that best fits the ship(s). Geometrical and Statistical descriptors are defined as well as their evolution along the various branches of the tree so that an efficient and robust detection can be done. Moreover, it has been observed that the performance of the whole system has led to satisfactory results (in terms of Precision and Recall).Actualmente, la detección de objetos es un campo de estudio recurrente i importante dentro del procesamiento de imagen y particularmente en teledetección. De hecho, ha evolucionado hacia una aplicación necesaria para nuestra sociedad ya que muchas tecnologías se basan en ella. Por lo tanto, el propósito de este proyecto es detectar un tipo específico de objetos: barcos en imaginería SAR (Synthetic Aperture Radar). Para lograrlo, se utiliza la representación max-tree juntamente con procesamiento de imagen y técnicas de clasificación. Además, se aplican técnicas de búsqueda a los max-trees para obtener el nodo (elemento unitario de dicha estructura) que mejor aproxima los barcos. Se definen descriptores geométricos y estadísticos así como su evolución a lo largo de las numerosas ramas del árbol de manera que se pueda realizar una detección eficiente y robusta. Asimismo, se ha observado la funcionalidad del sistema ha conducido a resultados satisfactorios (en términos de Precision and Recall).Actualment, la detecció d’objectes és un camp d’estudi recurrent i important dins del processament d’imatge i particularment en teledetecció. De fet, ha evolucionat cap a una aplicació necessària per a la nostra societat ja que moltes tecnologies es basen en ella. Per tant, el propòsit d’aquest projecte és detectar un tipus específic d’objectes: vaixells en imatgeria SAR (Synthetic Aperture Radar). Per aconseguir-ho, s’empra la representació max-tree juntament amb processament d’imatge i tècniques de classificació. A més, s’apliquen tècniques de recerca als max-trees per tal d’obtenir el node (element unitari d’aquesta estructura) que millor aproxima els vaixells. Es defineixen descriptors geomètrics i estadístics així com la seva evolució al llarg de les nombroses branques de l’arbre de manera que es puguin realitzar una detecció eficient i robusta. També, s’ha observat que la funcionalitat de tot el sistema ha conduït a resultats satisfactoris (en termes de Precision and Recall)

SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring SAR Remote Sensing Image Analysis with Max-tree Representation for Ship Monitoring

Este proyecto es un trabajo de investigación exploratorio sobre el uso de representación max-tree para imágenes radar (SAR). El potencial de este enfoque se va a estudiar en el contexto de la detección y el seguimiento de barcos con imágenes adquiridas desde satélites SAR.Nowadays, object detection is a recurrent and important field of study in Image Processing and particularly in Remote Sensing. In fact, it has evolved into a necessary application for our society as many technologies are based on it. Thus, the purpose of this project is to detect a specific type of objects: ships in SAR (Synthetic Aperture Radar) imagery. To do so, the max-tree representation together with image processing and classification techniques are employed. More precisely, search techniques are applied to the max-trees in order to obtain the node (i.e. unitary element of such structure) that best fits the ship(s). Geometrical and Statistical descriptors are defined as well as their evolution along the various branches of the tree so that an efficient and robust detection can be done. Moreover, it has been observed that the performance of the whole system has led to satisfactory results (in terms of Precision and Recall).Actualmente, la detección de objetos es un campo de estudio recurrente i importante dentro del procesamiento de imagen y particularmente en teledetección. De hecho, ha evolucionado hacia una aplicación necesaria para nuestra sociedad ya que muchas tecnologías se basan en ella. Por lo tanto, el propósito de este proyecto es detectar un tipo específico de objetos: barcos en imaginería SAR (Synthetic Aperture Radar). Para lograrlo, se utiliza la representación max-tree juntamente con procesamiento de imagen y técnicas de clasificación. Además, se aplican técnicas de búsqueda a los max-trees para obtener el nodo (elemento unitario de dicha estructura) que mejor aproxima los barcos. Se definen descriptores geométricos y estadísticos así como su evolución a lo largo de las numerosas ramas del árbol de manera que se pueda realizar una detección eficiente y robusta. Asimismo, se ha observado la funcionalidad del sistema ha conducido a resultados satisfactorios (en términos de Precision and Recall).Actualment, la detecció d’objectes és un camp d’estudi recurrent i important dins del processament d’imatge i particularment en teledetecció. De fet, ha evolucionat cap a una aplicació necessària per a la nostra societat ja que moltes tecnologies es basen en ella. Per tant, el propòsit d’aquest projecte és detectar un tipus específic d’objectes: vaixells en imatgeria SAR (Synthetic Aperture Radar). Per aconseguir-ho, s’empra la representació max-tree juntament amb processament d’imatge i tècniques de classificació. A més, s’apliquen tècniques de recerca als max-trees per tal d’obtenir el node (element unitari d’aquesta estructura) que millor aproxima els vaixells. Es defineixen descriptors geomètrics i estadístics així com la seva evolució al llarg de les nombroses branques de l’arbre de manera que es puguin realitzar una detecció eficient i robusta. També, s’ha observat que la funcionalitat de tot el sistema ha conduït a resultats satisfactoris (en termes de Precision and Recall)

Pilot contamination reduction in massive MIMO systems

Massive MIMO systems have been pointed out as one of the possible strategies to enhance system performance and reach the high data rates modern wireless communications demand. It represents a breakthrough in modern investigations given the new degrees of freedom and the extra dimensional space it provides. However, given the lack of channel knowledge, estimation must be employed. After this process, some interference due to the fast variation of the channel, which implies users sharing training sequences, is left. This is commonly referred to as pilot contamination and heavily compromises the throughput, specially in the large scale antennas regime. In this thesis, we will first study in detail this dramatic effect for later introducing different proposals that attempt to reduce its impact. In particular, we will start with the use of two main filters as basic processing. Next, allocation schemes to properly distribute users are discussed. Then, we will suggest projection based methods that transform the estimates with the purpose of canceling the undesired portions and strengthen the user destined signals. At the end of this analysis, it is shown numerically that the approaches presented behave well in these scenarios and help mitigate the interference created. This allows a faster and more robust transmission of information to take place in environments where pilot contamination is present

Stochastic geometry analysis and design of wireless powered MTC networks

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Machine-type-communications (MTC) are being crucial in the development of next generation mobile networks. Given that MTC devices are usually battery constrained, wireless power transfer (WPT) and energy harvesting (EH) have emerged as feasible options to enlarge the lifetime of the devices, leading to wireless powered networks. In that sense, we consider a setup where groups of sensors are served by a base station (BS), which is responsible for the WPT. Additionally, EH is used to collect energy from the wireless signals transmitted by other sensors. To characterize the energy obtained from both procedures, we model the sporadic activity of sensors as Bernoulli random variables and their positions with repulsive Matérn cluster processes. This way, the random activity and spatial distribution of sensors are introduced in the analysis of the energy statistics. This analysis can be useful for system design aspects such as energy allocation schemes or optimization of idle-active periods, among others. As an example of use of the developed analysis, we include the design of a WPT scheme under a proportional fair policy.The work presented in this paper was carried out within the framework of the project 5G&B RUNNER-UPC (TEC2016-77148-C2-1-R (AEI/FEDER, UE)), the research network RED2018-102668-T Red COMONSENS and the FPI grant BES-2017-079994, funded by the Spanish Ministry of Science, Innovation and Universities; and the grant 2017 SGR 578, funded by the Catalan Government (AGAUR, Secretaria d’Universitats i Recerca, Departament d’Empresa i Coneixement, Generalitat de Catalunya).Peer ReviewedPostprint (author's final draft