Adaptive Bandwidth Management and Joint Call Admission Control to Enhance System Utilization and QoS in Heterogeneous Wireless Networks

:The coexistence of different cellular networks in the same area necessitates joint radio resource management for enhanced QoS provisioning and efficient radio resource utilization. We propose adaptive bandwidth management and joint call admission control (JCAC) scheme for heterogeneous cellular networks. The objectives of the proposed adaptive JCAC scheme are to enhance average system utilization, guarantee QoS requirements of all accepted calls, and reduce new call blocking probability and handoff call dropping probability in heterogeneous wireless networks. We develop a Markov chain model for the adaptive JCAC scheme and derive new call blocking probability, handoff call dropping probability, and average system utilization. Performance of the proposed adaptive JCAC scheme is compared with that of nonadaptive JCAC scheme in the same heterogeneous wireless network. Results show an improvement in average system utilization of up to 20%. Results also show that connection-level QoS can be significantly improved by using the proposed adaptive JCAC scheme

Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing

Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’

A Modelling Framework for Common Radio Resource Management in Mobile Communication Systems

Im Rahmen dieser Arbeit wurde ein Modellierungsframework für die Untersuchung der technologieübergreifenden Verwaltung von Ressourcen heterogener Funkzugangsnetze (Common Radio Resource Management – CRRM) entwickelt. Die fünf Komponenten Umwelt (ENV), Nutzerendgerät (UE), Funkzugangssystem (RAS), CRRM-Informationsmanager (CRRM-IM) und CRRM-Entscheider (CRRM-D) können für die Gestaltung von zentralen bis dezentralen CRRM-Architekturen kombiniert werden. Sie decken damit ein weites Spektrum an möglichen CRRM-Einsatzszenarien ab. Dabei ermöglicht eine klare Struktur des zugrunde liegenden Modells die einfache Übertragung von Lösungsmethoden aus dem Gebiet der Multikriterienoptimierung. Ein integriertes Kostenmodell ermöglicht eine Kosten-/ Nutzen-Analyse für CRRM-Algorithmen und Architekturen. Die Verwendung eines hybriden Simulationsmodells ermöglicht die einfache Integration analytischer Funkzugangstechnologiemodelle und die Simulation komplexer Szenarien mit geringem Zeit- und Speicherbedarf. Hierbei liefern simulative Teilmodelle zeitgetreu neue Eingabeparameter für analytische Teilmodelle, deren Ausgabeparameter wiederum die Eingabeparameter der simulativen Teilmodelle sind. Nach diesem Modell wurde der auf OMNeT++ basierende diskrete ereignisorientierte Simulator HEKATE entwickelt. Der Simulator erwies sich als geeignet die zeiteffiziente Untersuchung von CRRM-Szenarien für UMTS- und GSM/EGPRS-Funkzugangssysteme durchzuführen.This work presents a modeling framework for the efficient evaluation of Common Radio Resource Management (CRRM). Centralized as well as decentralized scenarios can be clearly defined by five standard components, namely the radio access system (RAS), the environment (ENV), the user equipment (UE), the CRRM information manager (CRRM-IM), and the CRRM decider (CRRM-D). The clarity of the model enables an efficient investigation of CRRM algorithms based on multi-criteria optimization theory. The integrated cost model makes possible a cost-benefit investigation of different CRRM algorithms and architectures. A hybrid simulation model, where a simulation model and an analytical model operate in parallel over time, leads to low time and memory demands even for the simulation of complex scenarios. Additionally it allows for a convenient and straightforward integration of different analytical models for wireless network technologies. A discrete event simulator named HEKATE is based on this hybrid simulation model which has been implemented using OMNeT++. The scope of the proposed framework is demonstrated by the evaluation of realistic CRRM scenarios for UMTS and GSM/EGPRS

Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks

Hoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioeléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios. Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo tiempo. El algoritmo está formulado en términos de un problema de optimización multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-óptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras. El segundo algoritmo es un algoritmo de control de admisión conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologías al mismo tiempo. La principal diferencia con otros algorimos propuestos es que éstos últimos toman las dicisiones de admisión en cada tecnología por separado. Por ello, se necesita de algún mecanismo para seleccionar la tecnología a la que los usuarios se van a conectar. Por el contrario, la técnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogéneo. Por lo tanto, los usuarios no se enlazan con ninguna tecnología antes de ser admitidos.Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348Palanci

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed