Smart Beamforming for 5G and LTE Legacy Systems

The new generation of wireless communication systems is expected to provide many features and advances for a variety of use cases. In addition, the basis of 5G, being the Long Term Evolution (LTE) will be developed in parallel, meaning that further improvements need to be done in both technologies. In this work we propose two novel schemes, one for each generation, that provide potential benefits for the scenarios in which we are focused, in terms of Bit Error Rate (BER), Probability of Collision and/or achievable Rate as we will see. Firstly, massive Machine Type Communications (mMTC) is a multi-user and multi-service air interface which will be key in the next generation of communications. In this work, we propose a frame structure and signal processing techniques at the receiver needed to create the beamformers whose final objective is reducing the probability of collision between devices trying to get the resources. In the end, this will imply that more users can access to the media, as the receiver would be able to manage the collisions that will occur in the frequency domain, being the case of a Non-Orthogonal-Multiple-Access. Second, as there is an increasing interest in rapidly varying channels, we focus on this aspect proposing a frame structure for LTE in which we are able to track better the channel in case it has a low coherence time, that in combination with beamforming techniques will help the system to null interferences from other users and to increase the Signal to Interference and Noise Ratio (SINR) yielding to a lower BER. In the same chapter, in addition, we try to figure out which is the best power allocation with respect to the SNR to mix both training and data to even increase more the rate with respect to LTE without degrading the BER so much. We expect this work will be useful for the development of the technologies which are said that will improve our lifestyles

Two-Hop Multi-UAV Relay Network Optimization with Directional Antennas

In this paper, we consider the multi-UAV deployment problem for a two-hop relaying system. For a better network performance, UAVs carry directional antennas that are modeled by a realistic radiation pattern. The goal is to maximize the minimum user rates, and therefore achieve fairness in the network. We propose an iterative algorithm to optimize the TDMA scheduling in both hops, UAV trajectories, antenna beamwidths, and transmit power of the base station and relays. Simulation results show the throughput improvement as a result of optimizing the directional antenna radiation patterns. In addition, we derive the optimal power allocation, which combined with the beamwidth optimization yields to a much better performance.Comment: 30 page

EM based algorithms for Malaria diagnose via crowdsourcing

We live in a world in which medicine and technology are more united than ever. That is why in the last few years, lots of research groups initially dedicated to the development of technologies, have started to investigate in a field in which progresses are needed in order to protect the humanity against diseases, an this field is the medicine one. This work, following this trend, is focused on one of the diseases that affects the bast majority of tropical countries, Malaria. Along this final Degree Thesis, this disease will be the center of the work, firstly trying to sensitize the reader about its importance and after that, once the objectives have been defined, develop signal processing techniques and algorithms that in the end will count and detect malaria parasites via crowdsourcing, system that is explained in the Introduction chapter.Vivimos en un mundo en el que medicina y tecnología cada vez van más de la mano. Es por ello que durante los últimos años, muchos grupos de investigación inicialmente dedicados al desarrollo de tecnología, han desembarcado en un campo en el cual se necesitan avances para poder proteger al ser humano de enfermedades, es decir, el campo de la medicina. Este proyecto, siguiendo esta tendencia, se centra en una de las enfermedades que más afecta a países de zonas tropicales, la Malaria. A lo largo de este trabajo final de grado se hablará de esta enfermedad y se podrá sensibilizar al lector de su importancia. Una vez se han fijado los objetivos, se desarrollaran técnicas y algoritmos de procesado de señal cuya finalidad será la de contar y detectar parásitos de malaria mediante crowdsourcing, sistema explicado en la introducción.Vivim en un món el qual medicina i tecnologia cada cop estan més units. És per això que durant els últims anys, molts grups de recerca que inicialment es dedicaven al desenvolupament de tecnologia, s'han submergit en un camp en el qual es necessiten mes avenços per poder protegir l'ésser humà d'enfermetats, és a dir, el món de la medicina. Aquest treball, seguint aquesta tendència, es centra en una de les enfermetats que més afecta a països de zones tropicals, la Malaria. Durant aquest projecte de final de grau es parlarà sobre aquesta enfermetat i es podrà sensibilitzar el lector de la seva importancia. Un cop els objectius del treball han estat fixats es desenvoluparan tècniques i algorismes de processament del senyal la finalitat dels qual serà la de contar i detectar paràsits de malaria mitjançant crowdsourcing, sistema explicat a la introducció

Unsupervised ensemble classification with correlated decision agents

© 2019 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.Decision-making procedures when a set of individual binary labels is processed to produce a unique joint decision can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a Maximum a Posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.Peer ReviewedPostprint (author's final draft

Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation–Maximization (EM) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included.Peer ReviewedPreprin

Smart Beamforming for 5G and LTE Legacy Systems

The new generation of wireless communication systems is expected to provide many features and advances for a variety of use cases. In addition, the basis of 5G, being the Long Term Evolution (LTE) will be developed in parallel, meaning that further improvements need to be done in both technologies. In this work we propose two novel schemes, one for each generation, that provide potential benefits for the scenarios in which we are focused, in terms of Bit Error Rate (BER), Probability of Collision and/or achievable Rate as we will see. Firstly, massive Machine Type Communications (mMTC) is a multi-user and multi-service air interface which will be key in the next generation of communications. In this work, we propose a frame structure and signal processing techniques at the receiver needed to create the beamformers whose final objective is reducing the probability of collision between devices trying to get the resources. In the end, this will imply that more users can access to the media, as the receiver would be able to manage the collisions that will occur in the frequency domain, being the case of a Non-Orthogonal-Multiple-Access. Second, as there is an increasing interest in rapidly varying channels, we focus on this aspect proposing a frame structure for LTE in which we are able to track better the channel in case it has a low coherence time, that in combination with beamforming techniques will help the system to null interferences from other users and to increase the Signal to Interference and Noise Ratio (SINR) yielding to a lower BER. In the same chapter, in addition, we try to figure out which is the best power allocation with respect to the SNR to mix both training and data to even increase more the rate with respect to LTE without degrading the BER so much. We expect this work will be useful for the development of the technologies which are said that will improve our lifestyles

Smart Beamforming for 5G and LTE Legacy Systems

The new generation of wireless communication systems is expected to provide many features and advances for a variety of use cases. In addition, the basis of 5G, being the Long Term Evolution (LTE) will be developed in parallel, meaning that further improvements need to be done in both technologies. In this work we propose two novel schemes, one for each generation, that provide potential benefits for the scenarios in which we are focused, in terms of Bit Error Rate (BER), Probability of Collision and/or achievable Rate as we will see. Firstly, massive Machine Type Communications (mMTC) is a multi-user and multi-service air interface which will be key in the next generation of communications. In this work, we propose a frame structure and signal processing techniques at the receiver needed to create the beamformers whose final objective is reducing the probability of collision between devices trying to get the resources. In the end, this will imply that more users can access to the media, as the receiver would be able to manage the collisions that will occur in the frequency domain, being the case of a Non-Orthogonal-Multiple-Access. Second, as there is an increasing interest in rapidly varying channels, we focus on this aspect proposing a frame structure for LTE in which we are able to track better the channel in case it has a low coherence time, that in combination with beamforming techniques will help the system to null interferences from other users and to increase the Signal to Interference and Noise Ratio (SINR) yielding to a lower BER. In the same chapter, in addition, we try to figure out which is the best power allocation with respect to the SNR to mix both training and data to even increase more the rate with respect to LTE without degrading the BER so much. We expect this work will be useful for the development of the technologies which are said that will improve our lifestyles

EM based algorithms for Malaria diagnose via crowdsourcing

We live in a world in which medicine and technology are more united than ever. That is why in the last few years, lots of research groups initially dedicated to the development of technologies, have started to investigate in a field in which progresses are needed in order to protect the humanity against diseases, an this field is the medicine one. This work, following this trend, is focused on one of the diseases that affects the bast majority of tropical countries, Malaria. Along this final Degree Thesis, this disease will be the center of the work, firstly trying to sensitize the reader about its importance and after that, once the objectives have been defined, develop signal processing techniques and algorithms that in the end will count and detect malaria parasites via crowdsourcing, system that is explained in the Introduction chapter.Vivimos en un mundo en el que medicina y tecnología cada vez van más de la mano. Es por ello que durante los últimos años, muchos grupos de investigación inicialmente dedicados al desarrollo de tecnología, han desembarcado en un campo en el cual se necesitan avances para poder proteger al ser humano de enfermedades, es decir, el campo de la medicina. Este proyecto, siguiendo esta tendencia, se centra en una de las enfermedades que más afecta a países de zonas tropicales, la Malaria. A lo largo de este trabajo final de grado se hablará de esta enfermedad y se podrá sensibilizar al lector de su importancia. Una vez se han fijado los objetivos, se desarrollaran técnicas y algoritmos de procesado de señal cuya finalidad será la de contar y detectar parásitos de malaria mediante crowdsourcing, sistema explicado en la introducción.Vivim en un món el qual medicina i tecnologia cada cop estan més units. És per això que durant els últims anys, molts grups de recerca que inicialment es dedicaven al desenvolupament de tecnologia, s'han submergit en un camp en el qual es necessiten mes avenços per poder protegir l'ésser humà d'enfermetats, és a dir, el món de la medicina. Aquest treball, seguint aquesta tendència, es centra en una de les enfermetats que més afecta a països de zones tropicals, la Malaria. Durant aquest projecte de final de grau es parlarà sobre aquesta enfermetat i es podrà sensibilitzar el lector de la seva importancia. Un cop els objectius del treball han estat fixats es desenvoluparan tècniques i algorismes de processament del senyal la finalitat dels qual serà la de contar i detectar paràsits de malaria mitjançant crowdsourcing, sistema explicat a la introducció