End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose \name{}, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.Comment: IEEE Transactions on Machine Learning in Communications and Networking (TMLCN) 202

Aerial base station placement in temporary-event scenarios

Die Anforderungen an den Netzdatenverkehr sind in den letzten Jahren dramatisch gestiegen, was ein großes Interesse an der Entwicklung neuartiger Lösungen zur Erhöhung der Netzkapazität in Mobilfunknetzen erzeugt hat. Besonderes Augenmerk wurde auf das Problem der Kapazitätsverbesserung bei temporären Veranstaltungen gelegt, bei denen das Umfeld im Wesentlichen dynamisch ist. Um der Dynamik der sich verändernden Umgebung gerecht zu werden und die Bodeninfrastruktur durch zusätzliche Kapazität zu unterstützen, wurde der Einsatz von Luftbasisstationen vorgeschlagen. Die Luftbasisstationen können in der Nähe des Nutzers platziert werden und aufgrund der im Vergleich zur Bodeninfrastruktur höheren Lage die Vorteile der Sichtlinienkommunikation nutzen. Dies reduziert den Pfadverlust und ermöglicht eine höhere Kanalkapazität. Das Optimierungsproblem der Maximierung der Netzkapazität durch die richtige Platzierung von Luftbasisstationen bildet einen Schwerpunkt der Arbeit. Es ist notwendig, das Optimierungsproblem rechtzeitig zu lösen, um auf Veränderungen in der dynamischen Funkumgebung zu reagieren. Die optimale Platzierung von Luftbasisstationen stellt jedoch ein NP-schweres Problem dar, wodurch die Lösung nicht trivial ist. Daher besteht ein Bedarf an schnellen und skalierbaren Optimierungsalgorithmen. Als Erstes wird ein neuartiger Hybrid-Algorithmus (Projected Clustering) vorgeschlagen, der mehrere Lösungen auf der Grundlage der schnellen entfernungsbasierten Kapazitätsapproximierung berechnet und sie auf dem genauen SINR-basierten Kapazitätsmodell bewertet. Dabei werden suboptimale Lösungen vermieden. Als Zweites wird ein neuartiges verteiltes, selbstorganisiertes Framework (AIDA) vorgeschlagen, welches nur lokales Wissen verwendet, den Netzwerkmehraufwand verringert und die Anforderungen an die Kommunikation zwischen Luftbasisstationen lockert. Bei der Formulierung des Platzierungsproblems konnte festgestellt werden, dass Unsicherheiten in Bezug auf die Modellierung der Luft-Bodensignalausbreitung bestehen. Da dieser Aspekt im Rahmen der Analyse eine wichtige Rolle spielt, erfolgte eine Validierung moderner Luft-Bodensignalausbreitungsmodelle, indem reale Messungen gesammelt und das genaueste Modell für die Simulationen ausgewählt wurden.As the traffic demands have grown dramatically in recent years, so has the interest in developing novel solutions that increase the network capacity in cellular networks. The problem of capacity improvement is even more complex when applied to a dynamic environment during a disaster or temporary event. The use of aerial base stations has received much attention in the last ten years as the solution to cope with the dynamics of the changing environment and to supplement the ground infrastructure with extra capacity. Due to higher elevations and possibility to place aerial base stations in close proximity to the user, path loss is significantly smaller in comparison to the ground infrastructure, which in turn enables high data capacity. We are studying the optimization problem of maximizing network capacity by proper placement of aerial base stations. To handle the changes in the dynamic radio environment, it is necessary to promptly solve the optimization problem. However, we show that the optimal placement of aerial base stations is the NP-hard problem and its solution is non-trivial, and thus, there is a need for fast and scalable optimization algorithms. This dissertation investigates how to solve the placement problem efficiently and to support the dynamics of temporary events. First, we propose a novel hybrid algorithm (Projected Clustering), which calculates multiple solutions based on the fast distance-based capacity approximation and evaluates them on the accurate SINR-based capacity model, avoiding sub-optimal solutions. Second, we propose a novel distributed, self-organized framework (AIDA), which conducts a decision-making process using only local knowledge, decreasing the network overhead and relaxing the requirements for communication between aerial base stations. During the formulation of the placement problem, we found that there is still considerable uncertainty with regard to air-to-ground propagation modeling. Since this aspect plays an important role in our analysis, we validated state-of-the-art air-to-ground propagation models by collecting real measurements and chose the most accurate model for the simulations

Efficient Management of Flexible Functional Splits in 5G Second Phase Networks

The fifth mobile network generation (5G), which offers better data speeds, reduced latency, and a huge number of network connections, promises to improve the performance of the cellular network in practically every way available. A portion of the network operations are deployed in a centralized unit in the 5G radio access network (RAN) partially centralized design. By centralizing these functions, operational expenses are decreased and coordinating strategies are made possible. To link centralized units (CU) and distributed units (DU), and the DU to remote radio units (RRU), both the midhaul and fronthaul networks must have higher capacity. The necessary fronthaul capacity is also influenced by the fluctuating instantaneous user traffic. Consequently, the 5G RAN must be able to dynamically change its centralization level to the user traffic to maximize its performance. To try to relieve this fronthaul capacity it has been considered a more flexible distribution between the base band unit (BBU) (or CU and DU if enhanced common public radio interface (eCPRI) is considered) and the RRU. It may be challenging to provide high-speed data services in crowded areas, particularly when there is imperfect coverage or significant interference. Because of this, the macrocell deployment is insufficient. This problem for outdoor users could be resolved by the introduction of low-power nodes with a limited coverage area. In this context, this MSc dissertation explores, in an urban micro cell scenario model A (UMi_A) for three frequency bands (2.6 GHz, 3.5 GHz, and 5.62 GHz), the highest data rate achievable when a numerology zero is used. For this, it was necessary the implementation of the UMi_A in the 5G-air-simulator. Allowing the determination of the saturation level using the results for the packet loss ratio (PLR=2%). By assuming Open RAN (O-RAN) and functional splitting, the performance of two schedulers in terms of quality-of-service (QoS) were also studied. The QoS-aware modified largest weighted delay first (M-LWDF) scheduler and the QoS-unaware proportional fair (PF) scheduler. PLR was evaluated for both schedulers, whilst analyzing the impact of break point distance while changing the frequency band. The costs, revenues, profit in percentage terms, and other metrics were also estimated for the PF and M-LWDF schedulers when used video (VID) and video plus best effort (VID+BE), with or without consideration of the functional splits 7.2 and 6, for the three frequency bands. One concluded that the profit in percentage terms with functional split option 7.2 applied is always slightly higher than with functional split option 6. It reaches a maximum profit of 366.92% in the case of the M-LWDF scheduler, and 305.51% in the case of the PF scheduler, at a cell radius of 0.4 km for the 2.6 GHz frequency band, considering a price of the traffic of 0.0002 €/min.A quinta geração de redes móveis (5G), oferece ritmos de transmissão melhorados, atraso extremo-a-extremo reduzido, e um vasto número de ligações de rede. A 5G promete melhorar o desempenho das redes celulares em praticamente todos os aspectos relevantes. Uma parte da operação da rede é colocada numa unidade centralizada na rede de acesso de rádio (RAN) 5G com dimensionamento parcialmente centralizado. Ao centralizar estas funções, os custos operacionais decrescem, viabilizando-se as estratégias de coordenação. Para ligar as unidades centralizadas e unidades distribuídas, e por sua vez, unidades distribuidas e unidades de rádio remotas, ambos os midhaul e fronthaul devem ter uma capacidade mais elevada. A capacidade da fronthaul necessária é também influenciada pela flutuação do tráfego instantâneo dos utilizadores. Consequentemente, a RAN 5G deve ser capaz de alterar dinamicamente o seu nível de centralização para o tráfego de utilizadores, com objetivo de maximizar o seu desempenho. Para tentar aliviar o aumento da capacidade suportada pelo fronthaul, tem sido considerada uma distribuição mais flexível entre a unidade de banda base, BBU (ou unidade central e unidade distribuída se a interface de rádio pública comum melhorada, eCPRI, for considerada), e a unidade de rádio remota, RRU. Em áreas densamente povoadas, pode ser um desafio fornecer serviços de dados de elevada velocidade, particularmente quando existe uma cobertura deficiente ou interferência significativa. Por este motivo, o desenvolvimento de macrocélulas pode ser insuficiente, mas este problema para utilizadores em ambiente de exterior pode ser mitigado com a introdução de nós de potência reduzida com uma área de cobertura limitada. Neste contexto, esta dissertação de mestrado explora, num cenário urbano de microcélulas caracterizado pelo modelo A (UMi_A) para três bandas de frequência (2.6 GHz, 3.5 GHz, e 5.62 GHz), o débito binário máximo que se pode alcançar quando se utiliza numerologia zero. Para tal, foi necessária a implementação do UMi_A no 5G - air - simulator. Determinou-se o nivel de saturação, considerandose os resultados para a taxa de perda de pacotes (PLR=2%). Estudou-se o desempenho de dois escalonadores de pacotes em termos de qualidade de serviço (QoS), assumindo-se o OpenRAN (O-RAN) e as divisões funcionais (functionalsplitting). Um dos escalonadores é ciente da QoS, e é de atraso máximo-superior ponderado primeiro (M-LWDF), enquanto que o outro não é ciente da QoS, e é de justiça proporcional (PF). Avaliou-se o PLR para ambos os escalonadores de pacotes, estudando-se o impacto da distância de ponto de quebra (breakpointdistance), variando-se a banda de frequências. Foram também estimados os custos, proveitos, o lucro (em percentagem), e outras metricas, para os escalonadores PF e M-LWDF, considerando o vídeo (VID) e vídeo mais besteffort (VID+BE) como aplicações, com ou sem a consideração das divisões funcionais 7.2 e 6, para as três bandas de frequência. Concluiu-se que o lucro em termos percentuais, com a escolha da opção de divisão funcional 7.2, é sempre ligeiramente mais elevado do que com a opção de divisão funcional 6. Atingese um lucro máximo de 366,92% no caso do escalonador M-LWDF, e de 305,51% no caso do escalonador PF, para um raio de célula de 0,4 km, para a banda de frequência de 2,6 GHz, considerando-se um preço do tráfego de 0,0002 €/min

Advances in analytical models and applications for RFID, WSN and AmI systems

Experimentos llevados a cabo con el equipo de división de honor UCAM Volleyball Murcia.[SPA] Internet de las cosas (IoT) integra distintos elementos que actúan tanto como fuentes, como sumideros de información, a diferencia de la percepción que se ha tenido hasta ahora de Internet, centrado en las personas. Los avances en IoT engloban un amplio número de áreas y tecnologías, desde la adquisición de información hasta el desarrollo de nuevos protocolos y aplicaciones. Un concepto clave que subyace en el concepto de IoT, es el procesamiento de forma inteligente y autónoma de los flujos de información que se dispone. En este trabajo, estudiamos tres aspectos diferentes de IoT. En primer lugar, nos centraremos en la infraestructura de obtención de datos. Entre las diferentes tecnologías de obtención de datos disponibles en los sistemas IoT, la Identificación por Radio Frecuencia (RFID) es considerada como una de las tecnologías predominantes. RFID es la tecnología detrás de aplicaciones tales como control de acceso, seguimiento y rastreo de contenedores, gestión de archivos, clasificación de equipaje o localización de equipos. Con el auge de la tecnología RFID, muchas instalaciones empiezan a requerir la presencia de múltiples lectores RFID que operan próximos entre sí y conjuntamente. A estos escenarios se les conoce como dense reader environments (DREs). La coexistencia de varios lectores operando simultáneamente puede causar graves problemas de interferencias en el proceso de identificación. Uno de los aspectos claves a resolver en los RFID DREs consiste en lograr la coordinación entre los lectores. Estos problemas de coordinación son tratados en detalle en esta tesis doctoral. Además, dentro del área de obtención de datos relativa a IoT, las Redes de Sensores Inalámbricas (WSNs) desempeñan un papel fundamental. Durante la última década, las WSNs han sido estudiadas ampliamente de forma teórica, y la mayoría de problemas relacionados con la comunicación en este tipo de redes se han conseguido resolver de forma favorable. Sin embargo, con la implementación de WSNs en proyectos reales, han surgido nuevos problemas, siendo uno de ellos el desarrollo de estrategias realistas para desplegar las WSN. En este trabajo se estudian diferentes métodos que resuelven este problema, centrándonos en distintos criterios de optimización, y analizando las diferentes ventajas e inconvenientes que se producen al buscar una solución equilibrada. Por último, la Inteligencia Ambiental (AmI) forma parte del desarrollo de aplicaciones inteligentes en IoT. Hasta ahora, han sido las personas quienes han tenido que adaptarse al entorno, en cambio, AmI persigue crear entornos de obtención de datos capaces de anticipar y apoyar las acciones de las personas. AmI se está introduciendo progresivamente en diversos entornos reales tales como el sector de la educación y la salud, en viviendas, etc. En esta tesis se introduce un sistema AmI orientado al deporte que busca mejorar el entrenamiento de los atletas, siendo el objetivo prioritario el desarrollo de un asistente capaz de proporcionar órdenes de entrenamiento, basadas tanto en el entorno como en el rendimiento de los atletas. [ENG] Internet of Things (IoT) is being built upon many different elements acting as sources and sinks of information, rather than the previous human-centric Internet conception. Developments in IoT include a vast set of fields ranging from data sensing, to development of new protocols and applications. Indeed, a key concept underlying in the conception of IoT is the smart and autonomous processing of the new huge data flows available. In this work, we aim to study three different aspects within IoT. First, we will focus on the sensing infrastructure. Among the different kind of sensing technologies available to IoT systems, Radio Frequency Identification (RFID) is widely considered one of the leading technologies. RFID is the enabling technology behind applications such as access control, tracking and tracing of containers, file management, baggage sorting or equipment location. With the grow up of RFID, many facilities require multiple RFID readers usually operating close to each other. These are known as Dense Reader Environments (DREs). The co-existence of several readers operating concurrently is known to cause severe interferences on the identification process. One of the key aspects to solve in RFID DREs is achieving proper coordination among readers. This is the focus of the first part of this doctoral thesis. Unlike previous works based on heuristics, we address this problem through an optimization-based approach. The goal is identifying the maximum mean number of tags while network constraints are met. To be able to formulate these optimization problems, we have obtained analytically the mean number of identifications in a bounded -discrete or continuous- time period, an additional novel contribution of our work. Results show that our approach is overwhelmingly better than previous known methods. Along sensing technologies of IoT, Wireless Sensor Networks (WSNs) plays a fundamental role. WSNs have been largely and theoretically studied in the past decade, and many of their initial problems related to communication aspects have been successfully solved. However, with the adoption of WSNs in real-life projects, new issues have arisen, being one of them the development of realistic strategies to deploy WSNs. We have studied different ways of solving this aspect by focusing on different optimality criteria and evaluating the different trade-offs that occur when a balanced solution must be selected. On the one hand, deterministic placements subject to conflicting goals have been addressed. Results can be obtained in the form of Pareto-frontiers, allowing proper solution selection. On the other hand, a number of situations correspond to deployments were the nodes¿ position is inherently random. We have analyzed these situations leading first to a theoretical model, which later has been particularized to a Moon WSN survey. Our work is the first considering a full model with realistic properties such as 3D topography, propellant consumptions or network lifetime and mass limitations. Furthermore, development of smart applications within IoT is the focus of the Ambient Intelligence (AmI) field. Rather than having people adapting to the surrounding environment, AmI pursues the development of sensitive environments able to anticipate support in people¿s actions. AmI is progressively being introduced in many real-life environments like education, homes, health and so forth. In this thesis we develop a sport-oriented AmI system designed to improve athletes training. The goal is developing an assistant able to provide real-time training orders based on both environment and athletes¿ biometry, which is aimed to control the aerobic and the technical-tactical training. Validation experiments with the honor league UCAM Volleyball Murcia team have shown the suitability of this approach.[ENG] Internet of Things (IoT) is being built upon many different elements acting as sources and sinks of information, rather than the previous human-centric Internet conception. Developments in IoT include a vast set of fields ranging from data sensing, to development of new protocols and applications. Indeed, a key concept underlying in the conception of IoT is the smart and autonomous processing of the new huge data flows available. In this work, we aim to study three different aspects within IoT. First, we will focus on the sensing infrastructure. Among the different kind of sensing technologies available to IoT systems, Radio Frequency Identification (RFID) is widely considered one of the leading technologies. RFID is the enabling technology behind applications such as access control, tracking and tracing of containers, file management, baggage sorting or equipment location. With the grow up of RFID, many facilities require multiple RFID readers usually operating close to each other. These are known as Dense Reader Environments (DREs). The co-existence of several readers operating concurrently is known to cause severe interferences on the identification process. One of the key aspects to solve in RFID DREs is achieving proper coordination among readers. This is the focus of the first part of this doctoral thesis. Unlike previous works based on heuristics, we address this problem through an optimization-based approach. The goal is identifying the maximum mean number of tags while network constraints are met. To be able to formulate these optimization problems, we have obtained analytically the mean number of identifications in a bounded -discrete or continuous- time period, an additional novel contribution of our work. Results show that our approach is overwhelmingly better than previous known methods. Along sensing technologies of IoT, Wireless Sensor Networks (WSNs) plays a fundamental role. WSNs have been largely and theoretically studied in the past decade, and many of their initial problems related to communication aspects have been successfully solved. However, with the adoption of WSNs in real-life projects, new issues have arisen, being one of them the development of realistic strategies to deploy WSNs. We have studied different ways of solving this aspect by focusing on different optimality criteria and evaluating the different trade-offs that occur when a balanced solution must be selected. On the one hand, deterministic placements subject to conflicting goals have been addressed. Results can be obtained in the form of Pareto-frontiers, allowing proper solution selection. On the other hand, a number of situations correspond to deployments were the nodes¿ position is inherently random. We have analyzed these situations leading first to a theoretical model, which later has been particularized to a Moon WSN survey. Our work is the first considering a full model with realistic properties such as 3D topography, propellant consumptions or network lifetime and mass limitations. Furthermore, development of smart applications within IoT is the focus of the Ambient Intelligence (AmI) field. Rather than having people adapting to the surrounding environment, AmI pursues the development of sensitive environments able to anticipate support in people¿s actions. AmI is progressively being introduced in many real-life environments like education, homes, health and so forth. In this thesis we develop a sport-oriented AmI system designed to improve athletes training. The goal is developing an assistant able to provide real-time training orders based on both environment and athletes¿ biometry, which is aimed to control the aerobic and the technical-tactical training. Validation experiments with the honor league UCAM Volleyball Murcia team have shown the suitability of this approach.Universidad Politécnica de CartagenaPrograma de doctorado en Tecnología de la Información y de las Comunicacione