Approximate inference in massive MIMO scenarios with moment matching techniques

Mención Internacional en el título de doctorThis Thesis explores low-complexity inference probabilistic algorithms in high-dimensional Multiple-Input Multiple-Output (MIMO) systems and high order M-Quadrature Amplitude Modulation (QAM) constellations. Several modern communications systems are using more and more antennas to maximize spectral efficiency, in a new phenomena call Massive MIMO. However, as the number of antennas and/or the order of the constellation grow several technical issues have to be tackled, one of them is that the symbol detection complexity grows fast exponentially with the system dimension. Nowadays the design of massive MIMO low-complexity receivers is one important research line in MIMO because symbol detection can no longer rely on conventional approaches such as Maximum a Posteriori (MAP) due to its exponential computation complexity. This Thesis proposes two main results. On one hand a hard decision low-complexity MIMO detector based on Expectation Propagation (EP) algorithm which allows to iteratively approximate within polynomial cost the posterior distribution of the transmitted symbols. The receiver is named Expectation Propagation Detector (EPD) and its solution evolves from Minimum Mean Square Error (MMSE) solution and keeps per iteration the MMSE complexity which is dominated by a matrix inversion. Hard decision Symbol Error Rate (SER) performance is shown to remarkably improve state-of-the-art solutions of similar complexity. On the other hand, a soft-inference algorithm, more suitable to modern communication systems with channel codification techniques such as Low- Density Parity-Check (LDPC) codes, is also presented. Modern channel decoding techniques need as input Log-Likehood Ratio (LLR) information for each coded bit. In order to obtain that information, firstly a soft bit inference procedure must be performed. In low-dimensional scenarios, this can be done by marginalization over the symbol posterior distribution. However, this is not feasible at high-dimension. While EPD could provide this probabilistic information, it is shown that its probabilistic estimates are in general poor in the low Signal-to-Noise Ratio (SNR) regime. In order to solve this inconvenience a new algorithm based on the Expectation Consistency (EC) algorithm, which generalizes several algorithms such as Belief. Propagation (BP) and EP itself, was proposed. The proposed algorithm called Expectation Consistency Detector (ECD) maps the inference problem as an optimization over a non convex function. This new approach allows to find stationary points and tradeoffs between accuracy and convergence, which leads to robust update rules. At the same complexity cost than EPD, the new proposal achieves a performance closer to channel capacity at moderate SNR. The result reveals that the probabilistic detection accuracy has a relevant impact in the achievable rate of the overall system. Finally, a modified ECD algorithm is presented, with a Turbo receiver structure where the output of the decoder is fed back to ECD, achieving performance gains in all block lengths simulated. The document is structured as follows. In Chapter I an introduction to the MIMO scenario is presented, the advantages and challenges are exposed and the two main scenarios of this Thesis are set forth. Finally, the motivation behind this work, and the contributions are revealed. In Chapters II and III the state of the art and our proposal are presented for Hard Detection, whereas in Chapters IV and V are exposed for Soft Inference Detection. Eventually, a conclusion and future lines can be found in Chapter VI.Esta Tesis aborda algoritmos de baja complejidad para la estimación probabilística en sistemas de Multiple-Input Multiple-Output (MIMO) de grandes dimensiones con constelaciones M-Quadrature Amplitude Modulation (QAM) de alta dimensionalidad. Son diversos los sistemas de comunicaciones que en la actualidad están utilizando más y más antenas para maximizar la eficiencia espectral, en un nuevo fenómeno denominado Massive MIMO. Sin embargo los incrementos en el número de antenas y/o orden de la constelación presentan ciertos desafíos tecnológicos que deben ser considerados. Uno de ellos es la detección de los símbolos transmitidos en el sistema debido a que la complejidad aumenta más rápido que las dimensiones del sistema. Por tanto el diseño receptores para sistemas Massive MIMO de baja complejidad es una de las importantes líneas de investigación en la actualidad en MIMO, debido principalmente a que los métodos tradicionales no se pueden implementar en sistemas con decenas de antenas, cuando lo deseable serían centenas, debido a que su coste es exponencial. Los principales resultados en esta Tesis pueden clasificarse en dos. En primer lugar un receptor MIMO para decisión dura de baja complejidad basado en el algoritmo Expectation Propagation (EP) que permite de manera iterativa, con un coste computacional polinómico por iteración, aproximar la distribución a posteriori de los símbolos transmitidos. El algoritmo, denominado Expectation Propagation Detector (EPD), es inicializado con la solución del algoritmo Minimum Mean Square Error (MMSE) y mantiene el coste de este para todas las iteraciones, dominado por una inversión de matriz. El rendimiento del decisor en probabilidad de error de símbolo muestra ganancias remarcables con respecto a otros métodos en la literatura con una complejidad similar. En segundo lugar, un algoritmo que provee una estimación blanda, información que es más apropiada para los actuales sistemas de comunicaciones que utilizan codificación de canal, como pueden ser códigos Low-Density Parity-Check (LDPC). La información necesaria para estos decodificadores de canal es Log-Likehood Ratio (LLR) para cada uno de los bits codificados. En escenarios de bajas dimensiones se pueden calcular las marginales de la distribución a posteriori, pero en escenarios de grandes dimensiones no es viable, aunque EPD puede proporcionar este tipo de información a la entrada del decodificador, dicha información no es la mejor al estar el algoritmo pensado para detección dura, sobre todo se observa este fenómeno en el rango de baja Signal-to-Noise Ratio (SNR). Para solucionar este problema se propone un nuevo algoritmo basado en Expectation Consistency (EC) que engloba diversos algoritmos como pueden ser Belief Propagation (BP) y el algoritmo EP propuesto con anterioridad. El nuevo algoritmo llamado Expectation Consistency Detector (ECD), trata el problema como una optimización de una función no convexa. Esta aproximación permite encontrar los puntos estacionarios y la relación entre precisión y convergencia, que permitirán reglas de actualización más robustas y eficaces. Con la misma compleja que el algoritmo propuesto inicialmente, ECD permite rendimientos más próximos a la capacidad del canal en regímenes moderados de SNR. Los resultados muestran que la precisión tiene un gran efecto en la tasa que alcanza el sistema. Finalmente una versión modificada de ECD es propuesta en una arquitectura típica de los Turbo receptores, en la que la salida del decodificador es la entrada del receptor, y que permite ganancias en el rendimiento en todas las longitudes de código simuladas. El presente documento está estructurado de la siguiente manera. En el primer Capítulo I, se realiza una introducción a los sistemas MIMO, presentando sus ventajas, desventajas, problemas abiertos. Los modelos que se utilizaran en la tesis y la motivación con la que se inició esta tesis son expuestos en este primer capítulo. En los Capítulos II y III el estado del arte y nuestra propuesta para detección dura son presentados, mientras que en los Capítulos IV y V se presentan para detección suave. Finalmente las conclusiones que pueden obtenerse de esta Tesis y futuras líneas de investigación son expuestas en el Capítulo VI.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Juan José Murillo Fuentes.- Secretario: Gonzalo Vázquez Vilar.- Vocal: María Isabel Valera Martíne

Predicción de eventos deportivos empleando procesos gaussianos : tenis

En la sociedad actual el deporte es una de las actividades de ocio más recurrentes, desde unos años atrás la combinación de deporte y apuestas producen emoción, pero esto no siempre es sinónimo de ganancias. Por lo que proponemos un sistema que basado en eventos deportivos ocurridos, obteniendo una serie de características significativas, seamos capaces de predecir el resultado en un evento deportivo futuro, en este caso para el Tenis, para una vez calculadas las probabilidades poder apostar. En la creación del modelo de estimación de probabilidades emplearemos Procesos Gaussianos para Clasificación.Ingeniería Técnica en Sistemas de Telecomunicació

Improved performance of LDPC-coded MIMO systems with EP-based soft-decisions

The proceeding at: IEEE International Symposium on Information Theory (ISIT 2014), took place 2014, June 29-July 04, in Honolulu (Hawai)Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.This work has been partly funded by the Spanish Ministry of Science and Innovation with the projects GRE3NSYST (TEC2011-29006-C03-03) and ALCIT (TEC2012-38800-C03-01) and by the program CONSOLIDERINGENIO 2010 under the project COMONSENS (CSD 2008-00010).Publicad

Pedagogical Reflections on the Context of Teaching and Learning in the Departments of Basic Sciences of Fundación Universitaria del Área Andina

Los procesos de enseñanza y aprendizaje de las ciencias básicas van ligados a la construcción de escenarios de conocimiento contextualizados. De acuerdo con Gibbons y otros autores (1997, citado en González, 2016), la producción de conocimiento se refiere a la forma de acercarse al saber, el cual debe propender por la aplicación de los entornos y la interacción entre ellos y los sujetos. La presente reflexión busca dar cuenta de la experiencia que, como profesores de Física y Matemáticas, se ha tenido en la práctica docente en el Departamento de Ciencias Básicas de la Fundación Universitaria del Área Andina. Se reconocen elementos del hacer didáctico que garantizan aspectos fundamentales en la formación de los estudiantes que pasan por el Departamento en su etapa inicial; en este punto, la función de las ciencias básicas es de vital importancia en la apuesta formativa que posibilita el andamiaje y el recorrido que tendrá un futuro profesional. Reflexionar acerca de qué se enseña lleva a preguntarse por las necesidades actuales de la sociedad desde el aspecto científico, lo que permite vislumbrar la oportunidad de vincular las disciplinas científicas en las actividades cotidianas de los sujetos; por eso, el Departamento de Ciencias Básicas fundamenta su proceso la construcción del conocimiento científico, técnico, tecnológico y cultural. La formación profesional que ofrece el Departamento de Ciencias Básicas se orienta a la resolución de problemas en contextos por medio de los escenarios de la modelación, construir el pensamiento científico como un producto social de acercamiento a las ciencias.Abstract: The teaching and learning processes of the basic sciences are linked to the construction of contextualized knowledge scenarios, according to Gibbons and others (1997) cited by González (2016), the production of knowledge refers to the way of approaching knowledge, which should tend for the application of the environments and the interaction between them and the subjects. The present reflection seeks to account for the experience that as physics and mathematics professors have had in teaching practice in the department of basic sciences of Fundación Universitaria del Área Andina, recognizing elements of didactic work that guarantee fundamental aspects in the formation of Students who pass through the department in their initial training and as future professionals, where the function of basic sciences is of vital importance in the training bet and is the maker of specific constructs that enable scaffolding and the path that a professional future will have . Reflecting on what is taught, leads to asking about the current needs of society from the scientific aspect, which allows to glimpse the opportunity to link the scientific disciplines in the daily activities of the subjects, for this the department of basic sciences bases its process in the construction of scientific, technical, technological and cultural knowledge. For this reason, the professional training offered by the Department of Basic Sciences is oriented to the resolution of problems in contexts through the modeling scenarios, which allow to guide the construction of scientific thought as a social approach product the sciences

Expectation Propagation Detection for High-Order High-Dimensional MIMO Systems

Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-The-Art solutions reducing the symbol error rate at a reduced computational complexity.This work has been partly funded by the Spanish Ministry of Science and Innovation with the projects GRE3NSYST (TEC2011- 29006-C03-03) and ALCIT (TEC2012-38800-C03-01) and by the program CONSOLIDER-INGENIO 2010 under the project COMONSENS (CSD 2008- 00010).Publicad

Probabilistic MIMO symbol detection with expectation consistency approximate inference

In this paper, we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the expectation consistency (EC) framework, which describes the approximate inference problem as an optimization over a nonconvex function. EC generalizes algorithms such as belief propagation and expectation propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input-output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check channel code.This work has been partly supported by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects MIMOTEX (TEC2014-61776-EXP), CIES (RTC-2015-4213-7), ELISA (TEC2014-59255-C3-3R), FLUID (TEC2016-78434-C3-3-R) and CAIMAN (TEC2017-86921-C2-2-R), by the Juan de la Cierva program (IJCI-2014-19150), and by Comunidad de Madrid (project “CASI-CAM-CM" id. S2013/ICE-2845).Publicad

Progesterone receptor isoform A may regulate the effects of neoadjuvant aglepristone in canine mammary carcinoma

Background: Progesterone receptors play a key role in the development of canine mammary tumours, and recent research has focussed on their possible value as therapeutic targets using antiprogestins. Cloning and sequencing of the progesterone receptor gene has shown that the receptor has two isoforms, A and B, transcribed from a single gene. Experimental studies in human breast cancer suggest that the differential expression of progesterone receptor isoforms has implications for hormone therapy responsiveness. This study examined the effects of the antiprogestin aglepristone on cell proliferation and mRNA expression of progesterone receptor isoforms A and B in mammary carcinomas in dogs treated with 20 mg/Kg of aglepristone (n = 22) or vehicle (n = 5) twice before surgery. Results: Formalin-fixed, paraffin-embedded tissue samples taken before and after treatment were used to analyse total progesterone receptor and both isoforms by RT-qPCR and Ki67 antigen labelling. Both total progesterone receptor and isoform A mRNA expression levels decreased after treatment with aglepristone. Furthermore, a significant decrease in the proliferation index (percentage of Ki67-labelled cells) was observed in progesterone-receptor positive and isoform-A positive tumours in aglepristone-treated dogs. Conclusions: These findings suggest that the antiproliferative effects of aglepristone in canine mammary carcinomas are mediated by progesterone receptor isoform A

Dentin growth after direct pulp capping with the different fractions of plasma rich in growth factors (PRGF) vs MTA: experimental study in animal model

Background: The study aimed to evaluate the area of dentin growth in rabbit incisors after pulp capping with plasma rich in growth factors (PRGF) compared with mineral trioxide aggregate (MTA) by fluorescence. Methods: 27 upper and lower incisors of rabbits were divided into 4 groups: poor PRGF (F1) (n = 9 teeth), rich PRGF (F2) (n = 8 teeth), ProRoot MTA (positive control, n = 5 teeth) and untreated (NC) (negative control, n=5). Fluorochrome markers were injected 24 hours before surgery and the day before euthanasia, 28 days after the vital pulp therapy (VPT). Two transverse cuts were performed to every tooth: the first cut (A), 1 mm incisal to the gingival margin, and the second cut (B), 5 mm apical to the first cut. The sections were assessed with histomorphometric evaluation by fluorescence microscopy, comparing the dentin area between fluorescence marks and the total mineralized area. Results: The higher percentage of dentin growth was observed in the F2 group (B=63.25%, A=36.52%), followed by F1 (B=57.63%, A=30,12%) and MTA (B=38.64%, A=15,74%) The group with lowest percentage of dentin growth was the NC group (B=29.22%, A=7.82%). Significant difference (p <0.05) was found between F2 group and MTA, also statistically significant difference has been observed comparing dentin growth areas of NC group with F1 and F2 groups. Conclusions: The application of PRGF rich and poor fraction as a pulp capping material stimulated dentin formation significantly more intensively than MTA and NC

SARS-CoV-2 viral load in nasopharyngeal swabs is not an independent predictor of unfavorable outcome

The aim was to assess the ability of nasopharyngeal SARS-CoV-2 viral load at frst patient’s hospital evaluation to predict unfavorable outcomes. We conducted a prospective cohort study including 321 adult patients with confrmed COVID-19 through RT-PCR in nasopharyngeal swabs. Quantitative Synthetic SARS-CoV-2 RNA cycle threshold values were used to calculate the viral load in log10 copies/mL. Disease severity at the end of follow up was categorized into mild, moderate, and severe. Primary endpoint was a composite of intensive care unit (ICU) admission and/or death (n= 85, 26.4%). Univariable and multivariable logistic regression analyses were performed. Nasopharyngeal SARS-CoV-2 viral load over the second quartile (≥7.35 log10 copies/mL, p = 0.003) and second tertile (≥ 8.27 log10 copies/mL, p = 0.01) were associated to unfavorable outcome in the unadjusted logistic regression analysis. However, in the fnal multivariable analysis, viral load was not independently associated with an unfavorable outcome. Five predictors were independently associated with increased odds of ICU admission and/or death: age≥ 70 years, SpO2, neutrophils > 7.5 × ­103 /µL, lactate dehydrogenase≥ 300 U/L, and C-reactive protein≥ 100 mg/L. In summary, nasopharyngeal SARS-CoV-2 viral load on admission is generally high in patients with COVID-19, regardless of illness severity, but it cannot be used as an independent predictor of unfavorable clinical outcome

Risk factors for unfavorable outcome and impact of early post-transplant infection in solid organ recipients with COVID-19: A prospective multicenter cohort study

The aim was to analyze the characteristics and predictors of unfavorable outcomes in solid organ transplant recipients (SOTRs) with COVID-19. We conducted a prospective observational cohort study of 210 consecutive SOTRs hospitalized with COVID-19 in 12 Spanish centers from 21 February to 6 May 2020. Data pertaining to demographics, chronic underlying diseases, transplantation features, clinical, therapeutics, and complications were collected. The primary endpoint was a composite of intensive care unit (ICU) admission and/or death. Logistic regression analyses were performed to identify the factors associated with these unfavorable outcomes. Males accounted for 148 (70.5%) patients, the median age was 63 years, and 189 (90.0%) patients had pneumonia. Common symptoms were fever, cough, gastrointestinal disturbances, and dyspnea. The most used antiviral or host-targeted therapies included hydroxychloroquine 193/200 (96.5%), lopinavir/ritonavir 91/200 (45.5%), and tocilizumab 49/200 (24.5%). Thirty-seven (17.6%) patients required ICU admission, 12 (5.7%) suffered graft dysfunction, and 45 (21.4%) died. A shorter interval between transplantation and COVID-19 diagnosis had a negative impact on clinical prognosis. Four baseline features were identified as independent predictors of intensive care need or death: advanced age, high respiratory rate, lymphopenia, and elevated level of lactate dehydrogenase. In summary, this study presents comprehensive information on characteristics and complications of COVID-19 in hospitalized SOTRs and provides indicators available upon hospital admission for the identification of SOTRs at risk of critical disease or death, underlining the need for stringent preventative measures in the early post-transplant period