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

Traffic offloading in future, heterogeneous mobile networks

The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. In order to tackle this challenge, Mobile Network Operators (MNOs) aim to increase their networks' capacity by expanding their infrastructure, deploying more Base Stations (BSs). Particularly, the creation of Heterogeneous Networks (HetNets) and the application of traffic offloading through the dense deployment of low-power BSs, the small cells (SCs), is one promising solution to address the aforementioned explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the MNOs, the emergence of third parties that deploy small cell networks creates new business opportunities. Thus, the investigation of frameworks that facilitate the implementation of outsourced traffic offloading, the collaboration and the transactions among MNOs and third-party small cell owners, as well as the provision of participation incentives for all stakeholders is essential for the deployment of the necessary new infrastructure and capacity expansion. The aforementioned emergence of third-party content providers and their applications not only drives the increase in mobile data traffic, but also create new Quality of Service (QoS) as well as Quality of Experience (QoE) requirements that the MNOs need to guarantee for the satisfaction of their subscribers. Moreover, even though the MNOs accommodate this traffic, they do not get any monetary compensation or subsidization for the required capacity expansion. On the contrary, their revenues reduce continuously. To that end, it is necessary to research and design network and economic functionalities adapted to the new requirements, such as QoE-aware Radio Resource Management and Dynamic Pricing (DP) strategies, which both guarantee the subscriber satisfaction and maximization the MNO profit (to compensate the diminished MNOs' revenues and the increasing deployment investment). Following a thorough investigation of the state-of-the-art, a set of research directions were identified. This dissertation consists of contributions on network sharing and outsourced traffic offloading for the capacity enhancement of MNO networks, and the design of network and economic functions for the sustainable deployment and use of the densely constructed HetNets. The contributions of this thesis are divided into two main parts, as described in the following. The first part of the thesis introduces an innovative approach on outsourced traffic offloading, where we present a framework for the Multi-Operator Radio Access Network (MORAN) sharing. The proposed framework is based on an auction scheme used by a monopolistic Small Cell Operator (SCO), through which he leases his SC infrastructure to MNOs. As the lack of information on the future offered load and the auction strategies creates uncertainty for the MNOs, we designed a learning mechanism that assists the MNOs in their bid-placing decisions. Our simulations show that our proposal almost maximizes the social welfare, satisfying the involved stakeholders and providing them with participation incentives. The second part of the thesis researches the use of network and economic functions for MNO profit maximization, while guaranteeing the users' satisfaction. Particularly, we designed a model that accommodates a plethora of services with various QoS and QoE requirements, as well as diverse pricing, that is, various service prices and different charging schemes. In this model, we proposed QoE-aware user association, resource allocation and joint resource allocation and dynamic pricing algorithms, which exploit the QoE-awareness and the network's economic aspects, such as the profit. Our simulations have shown that our proposals gain substantial more profit compared to traditional and state-of-the-art solutions, while providing a similar or even better network performance.El aumento de los proveedores de contenido de terceros y la introducción de numerosas aplicaciones ha impulsado el crecimiento del tráfico de datos en redes móviles en los últimos años. Para hacer frente a este desafío, los operadores de redes móviles (Mobile Network Operators, MNOs) apuntan a aumentar la capacidad de sus redes mediante la expansión de su infraestructura y el despliegue de más estaciones base (BS). Particularmente, la creación de Redes Heterogéneas (Heterogenous Networks, HetNets) y la aplicación de descarga de tráfico a través del despliegue denso de BSs de baja potencia, las células pequeñas (small cells, SCs), es una solución prometedora para abordar el aumento del tráfico de datos explosivos antes mencionado. Debido a sus requisitos de implementación financiera, que los MNO no pudieron cumplir, la aparición de terceros que implementan redes de células pequeñas crea nuevas oportunidades comerciales. Por lo tanto, la investigación de marcos que faciliten la implementación de la descarga tercerizada de tráfico, la colaboración y las transacciones entre MNOs y terceros propietarios de células pequeñas, así como la provisión de incentivos de participación para todas las partes interesadas esencial para el despliegue de la nueva infraestructura necesaria y la expansión de la capacidad. La aparición antes mencionada de proveedores de contenido de terceros y sus aplicaciones no solo impulsa el aumento del tráfico de datos móviles, sino también crea nuevos requisitos de calidad de servicio (Quality of Service, QoS) y calidad de la experiencia (Quality of Experience, QoE) que los operadores de redes móviles deben garantizar para la satisfacción de sus suscriptores. Además, a pesar de que los operadores de redes móviles adaptan este tráfico, no obtienen ninguna compensación monetaria o subsidio por la expansión de capacidad requerida. Por el contrario, sus ingresos se reducen continuamente. Para ello, es necesario investigar y diseñar funcionalidades económicas y de red adaptadas a los nuevos requisitos, tales como las estrategias QoE-conscientes de gestión de recursos de radio y de precios dinámicos (Dynamic Pricing, DP), que garantizan la satisfacción del abonado y la maximización de la ganancia de operador móvil (para compensar los ingresos de los MNOs disminuidos y la creciente inversión de implementación). Después de una investigación exhaustiva del estado del arte, se identificaron un conjunto de direcciones de investigación. Esta disertación consiste en contribuciones sobre el uso compartido de redes y la descarga tercerizada de tráfico para la mejora de la capacidad de redes MNO, y el diseño de funciones económicas y de red para el despliegue y uso sostenible de las HetNets densamente construidas. Las contribuciones de esta tesis se dividen en dos partes principales, como se describe a continuación. La primera parte de la tesis presenta un enfoque innovador sobre la descarga subcontratada de tráfico, en el que presentamos un marco para el uso compartido de la red de acceso de radio de múltiples operadores (Multi-Operator RAN, MORAN). El marco propuesto se basa en un esquema de subasta utilizado por un operador monopólico de celda pequeña (Small Cell Operator, SCO), a través del cual arrienda su infraestructura SC a MNOs. Como la falta de información sobre la futura carga de red y las estrategias de subasta creaban incertidumbre para los MNO, diseñamos un mecanismo de aprendizaje que asiste a los MNO en sus decisiones de colocación de pujas. Nuestras simulaciones muestran que nuestra propuesta casi maximiza el bienestar social, satisfaciendo a las partes interesadas involucradas y proporcionándoles incentivos de participación. La segunda parte de la tesis investiga el uso de las funciones económicas y de red para la maximización de los beneficios de los MNOs, al tiempo que garantiza la satisfacción de los usuarios. Particularmente, diseñamos un modelo que acomoda una gran cantidad de servicios con diversos requisitos de QoS y QoE, tanto como diversos precios, es decir, varios precios de servicio y diferentes esquemas de cobro. En este modelo, propusimos algoritmos QoE-conscientes para asociación de usuarios, asignación de recursos y conjunta asignación de recursos y de fijación dinámica de precios, que explotan la conciencia de QoE y los aspectos económicos de la red, como la ganancia. Nuestras simulaciones han demostrado que nuestras propuestas obtienen un beneficio sustancial en comparación con las soluciones tradicionales y del estado del arte, a la vez que proporcionan un rendimiento de red similar o incluso mejor.Postprint (published version

Cognition-inspired 5G cellular networks: a review and the road ahead

Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics - including the cognitive and cooperative capabilities, network virtualization, and traffic offload - can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Quality of service differentiation for multimedia delivery in wireless LANs

Delivering multimedia content to heterogeneous devices over a variable networking environment while maintaining high quality levels involves many technical challenges. The research reported in this thesis presents a solution for Quality of Service (QoS)-based service differentiation when delivering multimedia content over the wireless LANs. This thesis has three major contributions outlined below: 1. A Model-based Bandwidth Estimation algorithm (MBE), which estimates the available bandwidth based on novel TCP and UDP throughput models over IEEE 802.11 WLANs. MBE has been modelled, implemented, and tested through simulations and real life testing. In comparison with other bandwidth estimation techniques, MBE shows better performance in terms of error rate, overhead, and loss. 2. An intelligent Prioritized Adaptive Scheme (iPAS), which provides QoS service differentiation for multimedia delivery in wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. The total bandwidth to be allocated is estimated using MBE. The priority level of individual stream is variable and dependent on stream-related characteristics and delivery QoS parameters. iPAS can be deployed seamlessly over the original IEEE 802.11 protocols and can be included in the IEEE 802.21 framework in order to optimize the control signal communication. iPAS has been modelled, implemented, and evaluated via simulations. The results demonstrate that iPAS achieves better performance than the equal channel access mechanism over IEEE 802.11 DCF and a service differentiation scheme on top of IEEE 802.11e EDCA, in terms of fairness, throughput, delay, loss, and estimated PSNR. Additionally, both objective and subjective video quality assessment have been performed using a prototype system. 3. A QoS-based Downlink/Uplink Fairness Scheme, which uses the stereotypes-based structure to balance the QoS parameters (i.e. throughput, delay, and loss) between downlink and uplink VoIP traffic. The proposed scheme has been modelled and tested through simulations. The results show that, in comparison with other downlink/uplink fairness-oriented solutions, the proposed scheme performs better in terms of VoIP capacity and fairness level between downlink and uplink traffic