Mobile Broadband Expansion Calls for More Spectrum or Base Stations - Analysis of the Value of Spectrum and the Role of Spectrum Aggregation

The breakthrough for mobile broadband is taking the mobile communications industry into a new phase. The number of mobile broadband users in the world exceeds 400 million, and the share of the population in Western Europe with mobile broadband is around 10 per cent and over 15 percent in Austria and Sweden. This development has been propelled by the extensive diffusion of mobile modems (dongles) for laptops and smartphones given users ubiquitous access to mobile internet. Consequently, traffic volumes in the mobile networks have grown immensely, and the mobile data traffic surpassed the mobile voice traffic in the world by the end of 2009, and in for example Sweden, over 75 percent of the mobile traffic is data. --

Control-data separation architecture for cellular radio access networks: a survey and outlook

Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided

Doctor of Philosophy

dissertationThe next generation mobile network (i.e., 5G network) is expected to host emerging use cases that have a wide range of requirements; from Internet of Things (IoT) devices that prefer low-overhead and scalable network to remote machine operation or remote healthcare services that require reliable end-to-end communications. Improving scalability and reliability is among the most important challenges of designing the next generation mobile architecture. The current (4G) mobile core network heavily relies on hardware-based proprietary components. The core networks are expensive and therefore are available in limited locations in the country. This leads to a high end-to-end latency due to the long latency between base stations and the mobile core, and limitations in having innovations and an evolvable network. Moreover, at the protocol level the current mobile network architecture was designed for a limited number of smart-phones streaming a large amount of high quality traffic but not a massive number of low-capability devices sending small and sporadic traffic. This results in high-overhead control and data planes in the mobile core network that are not suitable for a massive number of future Internet-of-Things (IoT) devices. In terms of reliability, network operators already deployed multiple monitoring sys- tems to detect service disruptions and fix problems when they occur. However, detecting all service disruptions is challenging. First, there is a complex relationship between the network status and user-perceived service experience. Second, service disruptions could happen because of reasons that are beyond the network itself. With technology advancements in Software-defined Network (SDN) and Network Func- tion Virtualization (NFV), the next generation mobile network is expected to be NFV-based and deployed on NFV platforms. However, in contrast to telecom-grade hardware with built-in redundancy, commodity off-the-shell (COTS) hardware in NFV platforms often can't be comparable in term of reliability. Availability of Telecom-grade mobile core network hardwares is typically 99.999% (i.e., "five-9s" availability) while most NFV platforms only guarantee "three-9s" availability - orders of magnitude less reliable. Therefore, an NFV-based mobile core network needs extra mechanisms to guarantee its availability. This Ph.D. dissertation focuses on using SDN/NFV, data analytics and distributed system techniques to enhance scalability and reliability of the next generation mobile core network. The dissertation makes the following contributions. First, it presents SMORE, a practical offloading architecture that reduces end-to-end latency and enables new functionalities in mobile networks. It then presents SIMECA, a light-weight and scalable mobile core network designed for a massive number of future IoT devices. Second, it presents ABSENCE, a passive service monitoring system using customer usage and data analytics to detect silent failures in an operational mobile network. Lastly, it presents ECHO, a distributed mobile core network architecture to improve availability of NFV-based mobile core network in public clouds

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

Game-theoretic infrastructure sharing in multioperator cellular networks

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The introduction of fourth-generation wireless technologies has fueled the rapid development of cellular networks, significantly increasing the energy consumption and the expenditures of mobile network operators (MNOs). In addition, network underutilization during low-traffic periods (e.g., night zone) has motivated a new business model, namely, infrastructure sharing, which allows the MNOs to have their traffic served by other MNOs in the same geographic area, thus enabling them to switch off part of their network. In this paper, we propose a novel infrastructure-sharing algorithm for multioperator environments, which enables the deactivation of underutilized base stations during low-traffic periods. Motivated by the conflicting interests of the MNOs and the necessity for effective solutions, we introduce a game-theoretic framework that enables the MNOs to individually estimate the switching-off probabilities that reduce their expected financial cost. Our approach reaches dominant strategy equilibrium, which is the strategy that minimizes the cost of each player. Finally, we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved in multioperator environments, incentivizing the MNOs to apply the proposed scheme.Peer ReviewedPostprint (author's final draft