Intelligent hybrid cheapest cost and mobility optimization RAT selection approaches for heterogeneous wireless networks

The evolution of wireless networks has led to the deployment of different Radio Access Technologies (RATs) such as UMTS Terrestrial Radio Access Network (UTRAN), Long Term Evolution (LTE), Wireless Local Area Network (WLAN) and Mobile Worldwide Interoperability for Microwave Access (WiMAX) which are integrated through a common platform. Common Radio Resource Management (CRRM) was proposed to manage radio resource utilization in heterogeneous wireless networks and to provide the required Quality of Service (QoS) for allocated calls. RAT selection algorithms are an integral part of the CRRM algorithms. Their role is to decide, when a new or Vertical Handover (VHO) call is requested, which of the available RATs is most suitable to fit the need of the incoming call and when to admit them. This paper extends our earlier work on the proposed intelligent mobility optimization and proposes an intelligent hybrid cheapest cost RAT selection approach which aims to increase users' satisfaction by allocation users that are looking for cheapest cost connections to a RAT that offers the cheapest cost of service. A comparison for the performance of centralized load-balancing, proposed and distributed cheapest cost and mobility optimization algorithms is presented. Simulation results show that the proposed intelligent algorithms perform better than the centralized load-balancing and the distributed algorithms. © 2014 Academy Publisher

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

Game theory for collaboration in future networks

Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.info:eu-repo/semantics/acceptedVersio

Enhancing cooperation in wireless networks using different concepts of game theory

PhDOptimizing radio resource within a network and across cooperating heterogeneous networks is the focus of this thesis. Cooperation in a multi-network environment is tackled by investigating network selection mechanisms. These play an important role in ensuring quality of service for users in a multi-network environment. Churning of mobile users from one service provider to another is already common when people change contracts and in a heterogeneous communication environment, where mobile users have freedom to choose the best wireless service-real time selection is expected to become common feature. This real time selection impacts both the technical and the economic aspects of wireless network operations. Next generation wireless networks will enable a dynamic environment whereby the nodes of the same or even different network operator can interact and cooperate to improve their performance. Cooperation has emerged as a novel communication paradigm that can yield tremendous performance gains from the physical layer all the way up to the application layer. Game theory and in particular coalitional game theory is a highly suited mathematical tool for modelling cooperation between wireless networks and is investigated in this thesis. In this thesis, the churning behaviour of wireless service users is modelled by using evolutionary game theory in the context of WLAN access points and WiMAX networks. This approach illustrates how to improve the user perceived QoS in heterogeneous networks using a two-layered optimization. The top layer views the problem of prediction of the network that would be chosen by a user where the criteria are offered bit rate, price, mobility support and reputation. At the second level, conditional on the strategies chosen by the users, the network provider hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR and optimizes the network coverage to support users who would otherwise not be serviced adequately. This forms an iterative cycle until a solution that optimizes the user satisfaction subject to the adjustments that the network provider can make to mitigate the binding constraints, is found and applied to the real network. The evolutionary equilibrium, which is used to 3 compute the average number of users choosing each wireless service, is taken as the solution. This thesis also proposes a fair and practical cooperation framework in which the base stations belonging to the same network provider cooperate, to serve each other‘s customers. How this cooperation can potentially increase their aggregate payoffs through efficient utilization of resources is shown for the case of dynamic frequency allocation. This cooperation framework needs to intelligently determine the cooperating partner and provide a rational basis for sharing aggregate payoff between the cooperative partners for the stability of the coalition. The optimum cooperation strategy, which involves the allocations of the channels to mobile customers, can be obtained as solutions of linear programming optimizations

SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS

A finales de los años noventa, y al comienzo del nuevo milenio, las redes inalámbricas han evolucionado bastante, pasando de ser sólo una tecnología prometedora para convertirse en un requisito para las actividades cotidianas en las sociedades desarrolladas. La infraestructura de transporte también ha evolucionado, ofreciendo comunicación a bordo para mejorar la seguridad vial y el acceso a contenidos de información y entretenimiento. Los requisitos de los usuarios finales se han hecho dependientes de la tecnología, lo que significa que sus necesidades de conectividad han aumentado debido a los diversos requisitos de las aplicaciones que se ejecutan en sus dispositivos móviles, tales como tabletas, teléfonos inteligentes, ordenadores portátiles o incluso ordenadores de abordo (On-Board Units (OBUs)) dentro de los vehículos. Para cumplir con dichos requisitos de conectividad, y teniendo en cuenta las diferentes redes inalámbricas disponibles, es necesario adoptar técnicas de Vertical Handover (VHO) para cambiar de red de forma transparente y sin necesidad de intervención del usuario. El objetivo de esta tesis es desarrollar algoritmos de decisión (Vertical Handover Decision Algorithms (VHDAs)) eficientes y escalables, optimizados para el contexto de las redes vehiculares. En ese sentido se ha propuesto, desarrollado y probado diferentes algoritmos de decisión basados en la infraestructura disponible en las actuales, y probablemente en las futuras, redes inalámbricas y redes vehiculares. Para ello se han combinado diferentes técnicas, métodos computacionales y modelos matemáticos, con el fin de garantizar una conectividad apropiada, y realizando el handover hacia las redes más adecuadas de manera a cumplir tanto con los requisitos de los usuarios como los requisitos de las aplicaciones. Con el fin de evaluar el contexto, se han utilizado diferentes herramientas para obtener información variada, como la disponibilidad de la red, el estado de la red, la geolocalizaciónMárquez Barja, JM. (2012). SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17869Palanci

Efficient joint call admission control and bandwidth management schemes for QoS provisioning in heterogeneous wireless networks

Includes abstract.Includes bibliographical references (leaves 150-157).Next generation wireless network (NGWN) will be heterogeneous where different radio access technologies (RATs) coexist. This coexistence of different RATs necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization. Joint call admission control (JCAC) algorithm is one of the joint radio resource management algorithms. The basic functions of a JCAC algorithm are to decide whether or not an incoming call can be accepted into a heterogeneous wireless network, and to determine which of the available RATs is most suitable to admit the incoming call. The objective of a JCAC algorithm is to guarantee the QoS requirements of all accepted calls and at the same time make the best use of the available radio resources. Traditional call admission control algorithms designed for homogeneous wireless networks do not provide a single solution to address the heterogeneous architecture, which characterizes NGWN. Consequently, there is need to develop JCAC algorithms for heterogeneous wireless networks. The thesis proposes three JCAC schemes for improving QoS and radio resource utilization, which are of primary concerns, in heterogeneous wireless networks. The first scheme combines adaptive bandwidth management and joint call admission control. The objectives of the first 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. The scheme consists of three components namely: joint call admission controller, bandwidth reservation unit, and bandwidth adaptation unit. Using Markov decision process, an analytical model is developed to evaluate the performance of the proposed scheme considering three performance metrics, which are new call blocking probability, handoff call dropping probability, and system utilization. Numerical results show that the proposed scheme improves system utilization and reduces both new call blocking probability and handoff call dropping probability. The second proposed JCAC scheme minimizes call blocking probability by determining the optimal call allocation policy among the available RATs. The scheme measures the arrival rates of different classes of calls into the heterogeneous wireless network. Using linear programming technique, the JCAC scheme determines the call allocation policy that minimizes call-blocking probability in the heterogeneous network. Numerical results show that the proposed scheme reduces call-blocking probability in the heterogeneous wireless network

An intelligent vertical handoff decision algorithm in next generation wireless networks

Philosophiae Doctor - PhDSeamless mobility is the missing ingredient needed to address the inefficient communication problems faced by the field workforces of service companies that are using field workforce automation solutions to streamline and optimise the operations of their field workforces in an increasingly competitive market place. The key enabling function for achieving seamless mobility and seamless service continuity is seamless handoffs across heterogeneous wireless access networks. A challenging issue in the multi-service next generation wireless network (NGWN) is to design intelligent and optimal vertical handoff decision algorithms, beyond traditional ones that are based on only signal strength, to determine when to perform a handoff and to provide optimal choice of access network technology among all available access networks for users equipped with multimode mobile terminals. The objective of the thesis research is to design such vertical handoff decision algorithms in order for mobile field workers and other mobile users equipped with contemporary multimode mobile devices to communicate seamlessly in the NGWN. In order to tackle this research objective, we used fuzzy logic and fuzzy inference systems to design a suitable handoff initiation algorithm that can handle imprecision and uncertainties in data and process multiple vertical handoff initiation parameters (criteria); used the fuzzy multiple attributes decision making method and context awareness to design a suitable access network selection function that can handle a tradeoff among many handoff metrics including quality of service requirements (such as network conditions and system performance), mobile terminal conditions, power requirements, application types, user preferences, and a price model; used genetic algorithms and simulated annealing to optimise the access network selection function in order to dynamically select the optimal available access network for handoff; and we focused in particular on an interesting use case: vertical handoff decision between mobile WiMAX and UMTS access networks. The implementation of our handoff decision algorithm will provide a network selection mechanism to help mobile users select the best wireless access network among all available wireless access networks, that is, one that provides always best connected services to user

Connecting Vehicles to the Internet - Strategic Data Transmission for Mobile Nodes using Heterogeneous Wireless Networks

With the advent of autonomous driving, the driving experience for users of connected vehicles changes, as they may enjoy their travel time with entertainment, or work productively. In our modern society, both require a stable Internet access. However, future mobile networks are not expected to be able to satisfy application Quality of Service (QoS) requirements as needed, e.g. during rush hours. To address this problem, this dissertation investigates data transmission strategies that exploit the potential of using a heterogeneous wireless network environment. To this end, we combine two so far distinct concepts, firstly, network selection and, secondly, transmission time selection, creating a joint time-network selection strategy. It allows a vehicle to plan delay-tolerant data transmissions ahead, favoring transmission opportunities with the best prospective flow-network matches. In this context, our first contribution is a novel rating model for perceived transmission quality, which assesses transmission opportunities with respect to application QoS requirement violations, traded off by monetary cost. To enable unified assessment of all data transmissions, it generalizes existing specialized rating models from network selection and transmission time selection and extends them with a novel throughput requirement model. Based on that, we develop a novel joint time-network selection strategy, Joint Transmission Planning (JTP), as our second contribution, planning optimized data transmissions within a defined time horizon. We compare its transmission quality to that of three predominant state-of-the-art transmission strategies, revealing that JTP outperforms the others significantly by up to 26%. Due to extensive scenario variation, we discover broad stability of JTP reaching 87-91% of the optimum. As JTP is a planning approach relying on prediction data, the transmission quality is strongly impaired when executing its plans under environmental changes. To mitigate this impact, we develop a transmission plan adaptation as our third contribution, modifying the planned current transmission online in order to comply with the changes. Even under strong changes of the vehicle movement and the network environment, it sustains 57%, respectively 36%, of the performance gain from planning. Finally, we present our protocol Mobility management for Vehicular Networking (MoVeNet), pooling available network resources of the environment to enable flexible packet dispatching without breaking connections. Its distributed architecture provides broad scalability and robustness against node failures. It complements control mechanisms that allow a demand-based and connection-specific trade-off between overhead and latency. Less than 9 ms additional round trip time in our tests, instant handover and 0 to 4 bytes per-packet overhead prove its efficiency. Employing the presented strategies and mechanisms jointly, users of connected vehicles and other mobile devices can significantly profit from the demonstrated improvements in application QoS satisfaction and reduced monetary cost

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio