Optimized network dimensioning and planning for WiMAX technology

In order to meet demands in mobile broadband and to bridge the digital divide a new technology, namely WiMAX, was introduced in 2004. However, in order to increase the financial return on the investment inWiMAX, service operators need to make every effort in designing and deploying the most cost-effective networks. This thesis presents a novel dimensioning technique for WiMAX technology which takes the dimensioning problem to a new level and produces more accurate results in comparison to the traditional methods. Furthermore, a novel decomposed optimization framework for the WiMAX network planning is introduced which subdivides the overall problem into three distinct stages consisting of the network dimensioning stage which besides the primary task of evaluating the financial requirements produces a good starting network solution for the subsequent stages (Stage 1), initial sectorization and configuration of the network (Stage 2) and final network configuration (Stage 3). The proposed framework also solves two fundamental problems, which are cell planning and frequency planning, simultaneously. The feasibility of the final network solutions are then evaluated by OPNET simulator.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimized network dimensioning and planning for WiMAX technology

In order to meet demands in mobile broadband and to bridge the digital divide a new technology, namely WiMAX, was introduced in 2004. However, in order to increase the financial return on the investment inWiMAX, service operators need to make every effort in designing and deploying the most cost-effective networks. This thesis presents a novel dimensioning technique for WiMAX technology which takes the dimensioning problem to a new level and produces more accurate results in comparison to the traditional methods. Furthermore, a novel decomposed optimization framework for the WiMAX network planning is introduced which subdivides the overall problem into three distinct stages consisting of the network dimensioning stage which besides the primary task of evaluating the financial requirements produces a good starting network solution for the subsequent stages (Stage 1), initial sectorization and configuration of the network (Stage 2) and final network configuration (Stage 3). The proposed framework also solves two fundamental problems, which are cell planning and frequency planning, simultaneously. The feasibility of the final network solutions are then evaluated by OPNET simulator

Lens antenna arrays: an efficient framework for sparse-aware large-MIMO communications

The recent increase in the demand for higher data transmission rates in wireless communications has entailed many implementation issues that can only be resolved by going through a full paradigm shift. Making use of the millimetric spectrum bands is a very attractive solution to the shortage of radio resources but, to garner all their potential, new techniques must be developed. Most of them are contained in the Massive Multiple Input Multiple Output (M-MIMO) framework: the idea of using very large antenna arrays for cellular communications. In this thesis, we propose the usage of Lens Antenna Arrays (LAA) to avoid the unbearable power and infrastructure costs posed by traditional M-MIMO architectures. This novel communication system exploits the angular-dependent power focusing capabilities of an electromagnetic lens to discern between waves with different angles of arrival and departure, without explicit signal processing. The work presented in this document motivates the use of LAAs in mmWave communications, studies some of their mathematical properties and proposes their application in noncoherent schemes. Numerical results validate the performance of this novel kind of systems and confirm their strengths in both multi-user and block fading settings. LAAs that use noncoherent methods appear to be very suitable for vehicular communications and densely populated cellular networks.En los últimos tiempos, el incremento en la demanda de mayor velocidad de transmisión de datos en redes de comunicación inalámbricas ha conllevado varios problemas de implementación que solo se podrán resolver a través de un cambio total de paradigma. Utilizar bandas milimétricas del espectro es una solución muy atractiva a la escasez de recursos de radio pero, para poder extraer todo su potencial, es necesario desarrollar nuevas técnicas. La mayor parte de éstas pasa por la infraestructura Massive Multiple Input Multiple Output (M-MIMO): la idea de usar matrices de antenas muy grandes para comunicaciones celulares. En esta tesis, proponemos el uso de matrices de antenas con lente, o Lens Antenna Arrays (LAA), para evitar los inasumibles costes energéticos y de instalación propios de las arquitecturas M-MIMO tradicionales. Este novedoso sistema de comunicaciones explota las capacidades de concentración de energía con dependencia angular de las lentes electromagnéticas para distinguir entre ondas con distintas direcciones de llegada y de salida, sin procesado de la señal explícito. El trabajo presentado en este documento motiva el uso de los LAAs en comunicaciones en bandas milimétricas (mmWave), estudia varias propiedades matemáticas y propone su aplicación en esquemas no coherentes. Resultados numéricos validan su ejecución y confirman sus fortalezas en entornos multiusuario y con desvanecimiento en bloque. Los LAAs que utilizan métodos no coherentes parecen ser idóneos para comunicaciones vehiculares y para redes celulares altamente pobladas.En els darrers temps, l'increment en la demanda de major velocitat de transmissió de dades en xarxes de comunicació inalàmbriques ha comportat diversos problemes d'implementació que tan sols es podran resoldre a través d'un canvi total de paradigma. Utilitzar les bandes mil·limètriques de l'espectre és una solució molt atractiva a l'escassetat de recursos de ràdio però, per tal d'extreure'n tot el seu potencial, és necessari desenvolupar noves tècniques. La majoria d'aquestes passa per la infraestructura Massive Multiple Input Multiple Output (M-MIMO): la idea d'utilitzar matrius d'antenes molt grans per a comunicacions cel·lulars. En aquesta tesi, proposem l'ús de matrius d'antenes amb lent, o Lens Antenna Arrays (LAA), per tal d'evitar els inassumibles costos energètics i d'instal·lació propis d'arquitectures M-MIMO tradicionals. Aquest innovador sistema de comunicacions explota les capacitats de concentració d'energia amb dependència angular de les lents electromagnètiques per tal de distingir entre ones amb diferents direccions d'arribada i de sortida, sense processament de senyal explícit. El treball presentat en aquest document motiva l'ús dels LAAs per comunicacions en bandes mil·limètriques (mmWave), n'estudia diverses propietats matemàtiques i proposa la seva aplicació en esquemes no coherents. Resultats numèrics en validen l'execució i confirmen les seves fortaleses en entorns multi-usuari i amb esvaïment en bloc. Els LAAs que utilitzen mètodes no coherents semblen ser idonis per a comunicacions vehiculars i per a xarxes cel·lulars altament poblades

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs