Design of static intercell interference coordination schemes for realistic lte-based cellular networks

Today, 3.5 and 4G systems including Long Term Evolution (LTE) and LTE-Advanced (LTE-A) support packet-based services and provide mobile broadband access for bandwidth-hungry applications. In this context of fast evolution, new and challenging technical issues must be e ectively addressed. The nal target is to achieve a signi cant step forward toward the improvement of the Quality of Experience (QoE). To that end, interference management has been recognized by the industry as a key enabler for cellular technologies based on OFDMA. Indeed, with a low frequency reuse factor, intercell interference (ICI) becomes a major concern since the Quality of Service (QoS) is not uniformly delivered across the network, it remarkably depends on user position. Hence, cell edge performance is an important issue in LTE and LTE-A. Intercell Interference Coordination (ICIC) encompasses strategies whose goal is to keep ICI at cell edges as low as possible. This alleviates the aforementioned situation. For this reason, the novelties presented in this Ph.D. thesis include not only developments of static ICIC mechanisms for data and control channels, but also e orts towards further improvements of the energy e ciency perspective. Based on a comprehensive review of the state of the art, a set of research opportunities were identi ed. To be precise, the need for exible performance evaluation methods and optimization frameworks for static ICIC strategies. These mechanisms are grouped in two families: the schemes that de ne constraints on the frequency domain and the ones that propose adjustments on the power levels. Thus, Soft- and Fractional Frequency Reuse (SFR and FFR, respectively) are identi ed as the base of the vast majority of static ICIC proposals. Consequently, during the rst part of this Ph.D. thesis, interesting insights into the operation of SFR and FFR were identi ed beyond well-known facts. These studies allow for the development of a novel statistical framework to evaluate the performance of these schemes in realistic deployments. As a result of the analysis, the poor performance of classic con gurations of SFR and FFR in real-world contexts is shown, and hence, the need for optimization is established. In addition, the importance of the interworking between static ICIC schemes and other network functionalities such as CSI feedback has also been identi ed. Therefore, novel CSI feedback schemes, suitable to operate in conjunction with SFR and FFR, have been developed. These mechanisms exploit the resource allocation pattern of these static ICIC techniques in order to improve the accuracy of the CSI feedback process. The second part is focused on the optimization of SFR and FFR. The use of multiobjective techniques is investigated as a tool to achieve e ective network-speci c optimization. The approach o ers interesting advantages. On the one hand, it allows for simultaneous optimization of several con icting criteria. On the other hand, the multiobjective nature results in outputs composed of several high quality (Pareto e cient) network con gurations, all of them featuring a near-optimal tradeo between the performance criteria. Multiobjective evolutionary algorithms allow employing complex mathematical structures without the need for relaxation, thus capturing accurately the system behavior in terms of ICI. The multiobjective optimization formulation of the problem aims at achieving e ective adjustment of the operational parameters of SFR and FFR both at cell level and network-wide. Moreover, the research was successfully extended to the control channels, both the PDCCH and ePDCCH. Finally, in an e ort to further improve the network energy e ciency (an aspect always considered throughout the thesis), the framework of Cell Switch O (CSO), having close connections with ICIC, is also introduced. By means of the proposed method, signi cant improvements with respect to traditional approaches, baseline con gurations, and previous proposals can be achieved. The gains are obtained in terms of energy consumption, network capacity, and cell edge performance.Actualmente los sistemas 3.5 y 4G tales como Long Term Evolution (LTE) y LTE-Advanced (LTE-A) soportan servicios basados en paquetes y proporcionan acceso de banda ancha m ovil para aplicaciones que requieren elevadas tasas de transmisi on. En este contexto de r apida evoluci on, aparecen nuevos retos t ecnicos que deben ser resueltos e cientemente. El objetivo ultimo es conseguir un salto cualitativo importante en la experiencia de usuario (QoE). Con tal n, un factor clave que ha sido reconocido en las redes celulares basadas en Orthogonal Frequency- Division Multiple Access (OFDMA) es la gesti on de interferencias. De hecho, la utilizaci on de un factor de reuso bajo permite una elevada e ciencia espectral pero a costa de una distribuci on de la calidad de servicio (QoS) que no es uniforme en la red, depende de la posici on del usuario. Por lo tanto, el rendimiento en los l mites de la celda se ve muy penalizado y es un problema importante a resolver en LTE y LTE-A. La coordinaci on de interferencias entre celdas (ICIC, del ingl es Intercell Interfe- rence Coordination) engloba las estrategias cuyo objetivo es mantener la interferencia intercelular (ICI) lo m as baja posible en los bordes de celda. Esto permite aliviar la situaci on antes mencionada. La contribuci on presentada en esta tesis doctoral incluye el dise~no de nuevos mecanismos de ICIC est atica para los canales de datos y control, as como tambi en mejoras desde el punto de vista de e ciencia energ etica. A partir de una revisi on completa del estado del arte, se identi caron una serie de retos abiertos que requer an esfuerzos de investigaci on. En concreto, la necesidad de m etodos de evaluaci on exibles y marcos de optimizaci on de las estrategias de ICIC est aticas. Estos mecanismos se agrupan en dos familias: los esquemas que de nen restricciones sobre el dominio de la frecuencia y los que proponen ajustes en los niveles de potencia. Es decir, la base de la gran mayor a de propuestas ICIC est aticas son la reutilizaci on de frecuencias de tipo soft y fraccional (SFR y FFR, respectivamente). De este modo, durante la primera parte de esta tesis doctoral, se han estudiado los aspectos m as importantes del funcionamiento de SFR y FFR, haciendo especial enfasis en las conclusiones que van m as all a de las bien conocidas. Ello ha permitido introducir un nuevo marco estad stico para evaluar el funcionamiento de estos sistemas en condiciones de despliegue reales. Como resultado de estos an alisis, se muestra el pobre desempe~no de SFR y FFR en despliegues reales cuando funcionan con sus con guraciones cl asicas y se establece la necesidad de optimizaci on. Tambi en se pone de mani esto la importancia del funcionamiento conjunto entre esquemas ICIC est aticos y otras funcionalidades de la red radio, tales como la informaci on que env an los usuarios sobre el estado de su canal downlink (feedback del CSI, del ingl es Channel State Information). De este modo, se han propuesto diferentes esquemas de feedback apropiados para trabajar conjuntamente con SFR y FFR. Estos mecanismos explotan el patr on de asignaci on de recursos que se utiliza en ICIC est atico para mejorar la precisi on del proceso. La segunda parte se centra en la optimizaci on de SFR y FFR. Se ha investigado el uso de t ecnicas multiobjetivo como herramienta para lograr una optimizaci on e caz, que es espec ca para cada red. El enfoque ofrece ventajas interesantes, por un lado, se permite la optimizaci on simult anea de varios criterios contradictorios. Por otro lado, la naturaleza multiobjetivo implica obtener como resultado con guraciones de red de elevada calidad (Pareto e cientes), todas ellas con un equilibrio casi- optimo entre las diferentes m etricas de rendimiento. Los algoritmos evolucionarios multiobjetivo permiten la utilizaci on de estructuras matem aticas complejas sin necesidad de relajar el problema, de este modo capturan adecuadamente su comportamiento en t erminos de ICI. La formulaci on multiobjetivo consigue un ajuste efectivo de los par ametros operacionales de SFR y FFR, tanto a nivel de celda como a nivel de red. Adem as, la investigaci on se extiende con resultados satisfactorios a los canales de control, PDCCH y ePDCCH. Finalmente, en un esfuerzo por mejorar la e ciencia energ etica de la red (un aspecto siempre considerado a lo largo de la tesis), se introduce en el an alisis global el apagado inteligente de celdas, estrategia con estrechos v nculos con ICIC. A trav es del m etodo propuesto, se obtienen mejoras signi cativas con respecto a los enfoques tradicionales y propuestas previas. Las ganancias se obtienen en t erminos de consumo energ etico, capacidad de la red, y rendimiento en el l mite de las celdas.Actualment els sistemes 3.5 i 4G tals com Long Term Evolution (LTE) i LTE- Advanced (LTE-A) suporten serveis basats en paquets i proporcionen acc es de banda ampla m obil per a aplicacions que requereixen elevades taxes de transmissi o. En aquest context de r apida evoluci o, apareixen nous reptes t ecnics que han de ser resolts e cientment. L'objectiu ultim es aconseguir un salt qualitatiu important en l'experi encia d'usuari (QoE). Amb tal , un factor clau que ha estat reconegut a les xarxes cel lulars basades en Orthogonal Frequency-Division Multiple Access (OFDMA) es la gesti o d'interfer encies. De fet, la utilizaci o d'un factor de re us baix permet una elevada e ci encia espectral per o a costa d'una distribuci o de la qualitat de servei (QoS) que no es uniforme a la xarxa, dep en de la posici o de l'usuari. Per tant, el rendiment en els l mits de la cel la es veu molt penalitzat i es un problema important a resoldre en LTE i LTE-A. La coordinaci o d'interfer encies entre cel les (ICIC, de l'angl es Intercell Interfe- rence Coordination) engloba les estrat egies que tenen com a objectiu mantenir la interfer encia intercel lular (ICI) el m es baixa possible en les vores de la cel la. Aix o permet alleujar la situaci o abans esmentada. La contribuci o presentada en aquesta tesi doctoral inclou el disseny de nous mecanismes de ICIC est atica per als canals de dades i control, aix com tamb e millores des del punt de vista d'e ci encia energ etica. A partir d'una revisi o completa de l'estat de l'art, es van identi car una s erie de reptes oberts que requerien esfor cos de recerca. En concret, la necessitat de m etodes d'avaluaci o exibles i marcs d'optimitzaci o de les estrat egies de ICIC est atiques. Aquests mecanismes s'agrupen en dues fam lies: els esquemes que de neixen restriccions sobre el domini de la freq u encia i els que proposen ajustos en els nivells de pot encia. Es a dir, la base de la gran majoria de propostes ICIC est atiques s on la reutilitzaci o de freq u encies de tipus soft i fraccional (SFR i FFR, respectivament). D'aquesta manera, durant la primera part d'aquesta tesi doctoral, s'han estudiat els aspectes m es importants del funcionament de SFR i FFR, fent especial emfasi en les conclusions que van m es enll a de les ben conegudes. Aix o ha perm es introduir un nou marc estad stic per avaluar el funcionament d'aquests sistemes en condicions de desplegament reals. Com a resultat d'aquestes an alisis, es mostra el pobre acompliment de SFR i FFR en desplegaments reals quan funcionen amb les seves con guracions cl assiques i s'estableix la necessitat d'optimitzaci o. Tamb e es posa de manifest la import ancia del funcionament conjunt entre esquemes ICIC est atics i altres funcionalitats de la xarxa radio, tals com la informaci o que envien els usuaris sobre l'estat del seu canal downlink (feedback del CSI, de l'angl es Channel State Information). D'aquesta manera, s'han proposat diferents esquemes de feedback apropiats per treballar conjuntament amb SFR i FFR. Aquests mecanismes exploten el patr o d'assignaci o de recursos que s'utilitza en ICIC est atic per millorar la precisi o del proc es. La segona part se centra en l'optimitzaci o de SFR i FFR. S'ha investigat l' us de t ecniques multiobjectiu com a eina per aconseguir una optimitzaci o e ca c, que es espec ca per a cada xarxa. L'enfocament ofereix avantatges interessants, d'una banda, es permet l'optimitzaci o simult ania de diversos criteris contradictoris. D'altra banda, la naturalesa multiobjectiu implica obtenir com resultat con guracions de xarxa d'elevada qualitat (Pareto e cients), totes elles amb un equilibri gaireb e optim entre les diferents m etriques de rendiment. Els algorismes evolucionaris multiobjectiu permeten la utilitzaci o d'estructures matem atiques complexes sense necessitat de relaxar el problema, d'aquesta manera capturen adequadament el seu comportament en termes de ICI. La formulaci o multiobjectiu aconsegueix un ajust efectiu dels par ametres operacionals de SFR i FFR, tant a nivell de cel la com a nivell de xarxa. A m es, la recerca s'est en amb resultats satisfactoris als canals de control, PDCCH i ePDCCH. Finalment, en un esfor c per millorar l'e ci encia energ etica de la xarxa (un aspecte sempre considerat al llarg de la tesi), s'introdueix en l'an alisi global l'apagat intel ligent de cel les, estrat egia amb estrets vincles amb ICIC. Mitjan cant el m etode proposat, s'obtenen millores signi catives pel que fa als enfocaments tradicionals i propostes pr evies. Els guanys s'obtenen en termes de consum energ etic, capacitat de la xarxa, i rendiment en el l mit de les cel les

Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities

Cell planning (CP) is the most important phase in the life cycle of a cellular system as it determines the operational expenditure, capital expenditure, as well as the long-term performance of the system. Therefore, it is not surprising that CP problems have been studied extensively for the past three decades for all four generations of cellular systems. However, the fact that small cells, a major component of future networks, are anticipated to be deployed in an impromptu fashion makes CP for future networks vis-a-vis 5G a conundrum. Furthermore, in emerging cellular systems that incorporate a variety of different cell sizes and types, heterogeneous networks (HetNets), energy efficiency, self-organizing network features, control and data plane split architectures (CDSA), massive multiple input multiple out (MIMO), coordinated multipoint (CoMP), cloud radio access network, and millimetre-wave-based cells plus the need to support Internet of Things (IoT) and device-to-device (D2D) communication require a major paradigm shift in the way cellular networks have been planned in the past. The objective of this paper is to characterize this paradigm shift by concisely reviewing past developments, analyzing the state-of-the-art challenges, and identifying future trends, challenges, and opportunities in CP in the wake of 5G. More specifically, in this paper, we investigate the problem of planning future cellular networks in detail. To this end, we first provide a brief tutorial on the CP process to identify the peculiarities that make CP one of the most challenging problems in wireless communications. This tutorial is followed by a concise recap of past research in CP. We then review key findings from recent studies that have attempted to address the aforementioned challenges in planning emerging networks. Finally, we discuss the range of technical factors that need to be taken into account while planning future networks and the promising research directions that necessitates the paradigm shift to do so

On the optimal operation of wireless networks

With the ever increasing mobile traffic in wireless networks, radio frequency spectrum is becoming limited and overcrowded. To address the radio frequency spectrum scarcity problem, researchers proposed advanced radio technology-Cognitive Radio to make use of the uncommonly used and under-utilized licensed bands to improve overall spectrum efficiency. Mobile service providers also deploy small base stations on the streets, into shopping center and users\u27 households in order to improve spectrum efficiency per area. In this thesis, we study cooperation schemes in cognitive radio networks as well as heterogeneous networks to reuse the existing radio frequency spectrum intelligently and improve network throughput and spectrum efficiency, reduce network power consumption and provide network failure protection capability. In the first work of the thesis, we study a multicast routing problem in Cognitive Ratio Networks (CRNs). In this work, all Secondary Users (SUs) are assumed not self interested and they are willing to provide relay service for source SUs. We propose a new network modeling method, where we model CRNs using a Multi-rate Multilayer Hyper-Graph (MMHG). Given a multicast session of the MMHG, our goal is to find the multicast routing trees that minimize the worst case end-to-end delay, maximize the multicast rate and minimize the number of transmission links used in the multicast tree. We apply two metaheuristic algorithms (Multi-Objective Ant Colony System optimization algorithm (MOACS) and Archived Multi-Objective Simulated Annealing Optimization Algorithm (AMOSA)) in solving the problem. We also study the scheduling problem of multicast routing trees obtained from the MMHG model. In the second work of the thesis, we study the cell outage compensation function of the self-healing mechanism using network cooperation scheme. In a heterogeneous network environment with densely deployed Femto Base Stations (FBSs), we propose a network cooperation scheme for FBSs using Coordinated Multi-Point (CoMP) transmission and reception with joint processing technique. Different clustering methods are studied to improve the performance of the network cooperation scheme. In the final work of the thesis, we study the user cooperative multi-path routing solution for wireless Users Equipment (UEs)\u27 streaming application using auction theory. We assume that UEs use multi-path transport layer service, and establish two paths for streaming events, one path goes through its cellular link, another path is established using a Wi-Fi connection with a neighbor UE. We study user coordinated multi-path routing solution with two different energy cost functions (LCF and EAC) and design user cooperative real-time optimization and failure protection operations for the streaming application. To stimulate UEs to participate into the user cooperation operation, we design a credit system enabled with auction mechanism. Simulation results in this thesis show that optimal cooperation operations among network devices to reuse the existing spectrum wisely are able to improve network performance considerably. Our proposed network modeling approach in CRN helps reduce the complicated multicast routing problem to a simple graph problem, and the proposed algorithms can find most of the optimal multicast routing trees in a short amount of time. In the second and third works, our proposed network cooperation and user cooperation approaches are shown to provide better UEs\u27 throughput compared to non-cooperation schemes. The network cooperation approach using CoMP provides failure compensation capability by preventing the system sum rate loss from having the same speed of radio resource loss, and this is done without using additional radio resources and will not have a significant adverse effect on the performance of other UEs. The user cooperation approach shows great advantage in improving service rate, improving streaming event success rate and reducing energy consumption compared to non-cooperation solution

Spectrally and Energy Efficient Radio Resource Management for Multi-Operator Shared Networks

Commercial mobile communication systems are mainly based on licensed frequency spectrum, and the license is very expensive as the spectrum is a sparse wireless resource. Therefore, sharing this wireless resource is an essential requirement not only at the present but also in the future considering trends like connectivity for everybody and everything. In this thesis, we study the sharing of wireless resources with different approaches for realizing fair, efficient, and predictable sharing solutions in a controlled manner. The efficient use of wireless channel resources is an important target to reduce the costs of network operation and deployment. To achieve this, we need practical scheduling algorithms for wireless resources, out of which several of them will be presented and analyzed in this work. Different optimization frameworks for the spectral efficiency utility are presented, with an individual focus on guaranteeing resource or rate fairness among the operators in a network with shared radio resources. Thus, the presented proposals will help the mobile network operators to overcome the issues of losing network control and traceability of used wireless resources in a shared environment. Besides this, emerging vertical industries, such as automotive, healthcare, industry 4.0, internet of things (IoT) industries will put a certain burden on the wireless networks asking for guaranteed service level requirement from the mobile network operators. In this regard, this thesis provides the necessary methods addressing these challenges with the help of scheduling methods which are based on the joint optimization of spectral and energy efficiency. Thus, wireless networks will be enabled as a service function in a controlled and scalable way for new emerging markets. Furthermore, the presented solutions t well with the requirements of fifth generation (5G) network slicing

Optimización de problemas de varios objetivos desde un enfoque de eficiencia energética aplicado a redes celulares heterogéneas 5G usando un marco de conmutación de celdas pequeñas

This Ph.D. dissertation addresses the Many-Objective Optimization Problem (MaOP) study to reduce the inter-cell interference and the power consumption for realistic Centralized, Collaborative, Cloud, and Clean Radio Access Networks (C-RANs). It uses the Cell Switch-Off (CSO) scheme to switch-off/on Remote Radio Units (RRUs) and the Coordinated Scheduling (CS) technique to allocate resource blocks smartly. The EF1-NSGA-III (It is a variation of the NSGA-III algorithm that uses the front 1 to find extreme points at the normalization procedure extended in this thesis) algorithm is employed to solve a proposed Many-Objective Optimization Problem (MaOP). It is composed of four objective functions, four constraints, and two decision variables. However, the above problem is redefined to have three objective functions to see the performance comparison between the NSGA-II and EF1-NSGA-III algorithms. The OpenAirInterface (OAI) platform is used to evaluate and validate the performance of an indoor coverage system because most of the user-end equipment of next-generation cellular networks will be in an indoor environment. It constitutes the fastest growing 5G open-source platform that implements 3GPP technology on general-purpose computers, fast Ethernet transport ports, and Commercial-Off-The-Shelf (COTS) software-defined radio hardware. This document is composed of five contributions. The first one is a survey about testbed, emulators, and simulators for 4G/5G cellular networks. The second one is the extension of the KanGAL's NSGA-II code to implement the EF1-NSGA-III, adaptive EF1-NSGA-III (A-EF1-NSGA-III), and efficient adaptive EF1-NSGA-III (A$^2$-EF1-NSGA-III). They support up to 10 objective functions, manage real, integer, and binary decision variables, and many constraints. The above algorithms outperform other works in terms of the Inverted Generational Distance (IGD) metric. The third contribution is the implementation of real-time emulation methodologies for C-RANs using a frequency domain representation in OAI. It improves the average computation time 10-fold compared to the time domain without using Radio Frequency hardware and avoids their uncertainties. The fourth one is the implementation of the Coordination Scheduling (CS) technique as a proof-of-concept to validate the advantages of frequency domain methodologies and to allocate resource blocks dynamically among RRUs. Finally, a many-objective optimization problem is defined and solved with evolutionary algorithms where diversity is managed based on crowded-distance and reference points to reduce the power consumption for C-RANs. The solutions obtained are considered to control the scheduling task at the Radio Cloud Center (RCC) and to switch RRUs.Este disertación aborda el estudio del problema de optimización de varios objetivos (MaOP) para reducir la interferencia entre células y el consumo de energía para redes de acceso de radio en tiempo real, colaborativas, en la nube y limpias (C-RAN). Utiliza el esquema de conmutacion de celdas (CSO) para apagar / encender unidades de radio remotas (RRU) y la técnica de programación coordinada (CS) para asignar bloques de recursos de manera inteligente. El algoritmo EF1-NSGA-III (es una variación del algoritmo NSGA-III que usa el primer frente de pareto para encontrar puntos extremos en el procedimiento de normalización extendido en esta tesis) se utiliza para resolver un problema de optimización de varios objetivos (MaOP) propuesto. Se compone de cuatro funciones objetivos, cuatro restricciones y dos variables de decisión. Sin embargo, el problema anterior se redefine para tener tres funciones objetivas para ver la comparación de rendimiento entre los algoritmos NSGA-II y EF1-NSGA-III. La plataforma OpenAirInterface (OAI) se utiliza para evaluar y validar el rendimiento de un sistema de cobertura en interiores porque la mayoría del equipos móviles de las redes celulares de próxima generación estarán en un entorno interior. Ella constituye la plataforma de código abierto 5G de más rápido crecimiento que implementa la tecnología 3GPP en computadoras de uso general, puertos de transporte Ethernet rápidos y hardware de radio definido por software comercial (COTS). Este documento se compone de cinco contribuciones. La primera es una estudio sobre banco de pruebas, emuladores y simuladores para redes celulares 4G / 5G. El segundo es la extensión del código NSGA-II de KanGAL para implementar EF1-NSGA-III, EF1-NSGA-III adaptativo (A-EF1-NSGA-III) y EF1-NSGA-III adaptativo eficiente (A $^ 2$ -EF1-NSGA-III). Admiten hasta 10 funciones objetivas, gestionan variables de decisión reales, enteras y binarias, y muchas restricciones. Los algoritmos anteriores superan a otros trabajos en términos de la métrica de distancia generacional invertida (IGD). La tercera contribución es la implementación de metodologías de emulación en tiempo real para C-RAN utilizando una representación de dominio de frecuencia en OAI. Mejora el tiempo de cálculo promedio 10 veces en comparación con el dominio del tiempo sin usar hardware de radiofrecuencia y evita sus incertidumbres. El cuarto es la implementación de la técnica de Programación de Coordinación (CS) como prueba de concepto para validar las ventajas de las metodologías de dominio de frecuencia y asignar bloques de recursos dinámicamente entre las RRU. Finalmente, un problema de optimización de muchos objetivos se define y resuelve con algoritmos evolutivos en los que la diversidad se gestiona en función de la distancia de crouding y los puntos de referencia para reducir el consumo de energía de las C-RAN. Las soluciones obtenidas controlan la tarea de programación en Radio Cloud Center (RCC) y conmutan las RRU.Proyecto personal: Redes celulares de próxima generaciónDoctorad

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Cost efficient 5G heterogeneous base station deployment using meta-heuristics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOver the last two decades, the telecommunication industry has witnessed sustained growth in the number of mobile user devices driven by the introduction of data services, the take-off of the internet and smart user equipment. This growth, which is forecasted to continue, has continued to push the data transfer capacity requirement on mobile networks and has motivated research into the design of 5th generation (5G) mobile networks. A key concern in the design of 5G is the infrastructure and power consumption cost of the base station network which is expected to be significantly more advanced and dense than that of existing conventional mobile networks. This thesis presents an optimisation framework for the cost efficient design of 5G base station networks, based on the application of meta-heuristic algorithms. The presented optimisation framework is centred on the ability to exploit three key technologies of 5G, a heterogonous base station network with small-cells, multi-antenna spatial multiplexing MIMO and cell range extension. The framework includes mathematical integer programming models for supporting the decisions about the optimal base station topology in a 5G mobile network and provides a clear core for the application of meta-heuristics for optimising 5G base station deployment. The core optimisation framework includes the definition of solution encoding/decoding and fitness mechanisms. To increase power consumption awareness of base station network design, an independent base station deployment strategy has been presented and evaluated. Simulation results show that the strategy can improve base station network design power consumption by as much as 34%. The work in this thesis has been extensively evaluated using a simulated 5G mobile network system model. Evaluations of algorithms have been performed through empirical measurements. The main contribution of this thesis is the definition of a clear framework for application fitness based heuristic search in the design of 5G mobile networks

Resource Allocation for Multiple-Input and Multiple-Output Interference Networks

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

4G/5G cellular networks metrology and management

La prolifération d'applications et de services sophistiqués s'accompagne de diverses exigences de performances, ainsi que d'une croissance exponentielle du trafic pour le lien montant (uplink) et descendant (downlink). Les réseaux cellulaires tels que 4G et 5G évoluent pour prendre en charge cette quantité diversifiée et énorme de données. Le travail de cette thèse vise le renforcement de techniques avancées de gestion et supervision des réseaux cellulaires prenant l'explosion du trafic et sa diversité comme deux des principaux défis dans ces réseaux. La première contribution aborde l'intégration de l'intelligence dans les réseaux cellulaires via l'estimation du débit instantané sur le lien montant pour de petites granularités temporelles. Un banc d'essai 4G temps réel est déployé dans ce but de fournir un benchmark exhaustif des métriques de l'eNB. Des estimations précises sont ainsi obtenues. La deuxième contribution renforce le découpage 5G en temps réel au niveau des ressources radio dans un système multicellulaire. Pour cela, deux modèles d'optimisation ont été proposés. Du fait de leurs temps d'exécution trop long, des heuristiques ont été développées et évaluées en comparaisons des modèles optimaux. Les résultats sont prometteurs, les deux heuristiques renforçant fortement le découpage du RAN en temps réel.The proliferation of sophisticated applications and services comes with diverse performance requirements as well as an exponential traffic growth for both upload and download. The cellular networks such as 4G and 5G are advocated to support this diverse and huge amount of data. This thesis work targets the enforcement of advanced cellular network supervision and management techniques taking the traffic explosion and diversity as two main challenges in these networks. The first contribution tackles the intelligence integration in cellular networks through the estimation of users uplink instantaneous throughput at small time granularities. A real time 4G testbed is deployed for such aim with an exhaustive metrics benchmark. Accurate estimations are achieved.The second contribution enforces the real time 5G slicing from radio resources perspective in a multi-cell system. For that, two exact optimization models are proposed. Due to their high convergence time, heuristics are developed and evaluated with the optimal models. Results are promising, as two heuristics are highly enforcing the real time RAN slicing