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

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Performances des Réseaux LTE

Poussé par la demande croissante de services à haut débit sans fil, Long Term Evolution (LTE) a émergé comme une solution prometteuse pour les communications mobiles. Dans plusieurs pays à travers le monde, la mise en oeuvre de LTE est en train de se développer. LTE offre une architecture tout-IP qui fournit des débits élevés et permet une prise en charge efficace des applications de type multimédia. LTE est spécifié par le 3GPP ; cette technologie fournit une architecture capable de mettre en place des mécanismes pour traiter des classes de trafic hétérogènes comme la voix, la vidéo, les transferts de fichier, les courriers électroniques, etc. Ces classes de flux hétérogènes peuvent être gérées en fonction de la qualité de service requise mais aussi de la qualité des canaux et des conditions environnementales qui peuvent varier considérablement sur une courte échelle de temps. Les standards du 3GPP ne spécifient pas l algorithmique de l allocation des ressources du réseau d accès, dont l importance est grande pour garantir performance et qualité de service (QoS). Dans cette thèse, nous nous focalisons plus spécifiquement sur la QoS de LTE sur la voie descendante. Nous nous concentrons alors sur la gestion des ressources et l ordonnancement sur l interface radio des réseaux d accès. Dans une première partie, nous nous sommes intéressés à des contextes de macro-cellules. Le premier mécanisme proposé pour l allocation des ressources combine une méthode de jetons virtuels et des ordonnanceurs opportunistes. Les performances obtenues sont très bonnes mais n assurent pas une très bonne équité. Notre seconde proposition repose sur la théorie des jeux, et plus spécifiquement sur la valeur de Shapley, pour atteindre un haut niveau d équité entre les différentes classes de services au détriment de la qualité de service. Cela nous a poussé, dans un troisième mécanisme, à combiner les deux schémas. La deuxième partie de la thèse est consacrée aux femto-cellules (ou femtocells) qui offrent des compléments de couverture appréciables. La difficulté consiste alors à étudier et à minimiser les interférences. Notre premier mécanisme d atténuation des interférences est fondé sur le contrôle de la puissance de transmission. Il fonctionne en utilisant la théorie des jeux non coopératifs. On effectue une négociation constante entre le débit et les interférences pour trouver un niveau optimal de puissance d émission. Le second mécanisme est centralisé et utilise une approche de division de la bande passante afin d obliger les femtocells à ne pas utiliser les mêmes sous-bandes évitant ainsi les interférences. Le partage de bande passante et l allocation sont effectués en utilisant sur la théorie des jeux (valeur de Shapley) et en tenant compte du type d application. Ce schéma réduit les interférences considérablement. Tous les mécanismes proposés ont été testés et évalués dans un environnement de simulation en utilisant l outil LTE-Sim au développement duquel nous avons contribué.Driven by the growing demand for high-speed broadband wireless services, Long term Evolution (LTE) technology has emerged as a competitive alternative to mobile communications solution. In several countries around the world, the implementation of LTE has started. LTE offers an IP-based framework that provides high data rates for multimedia applications. Moreover, based on the 3GPP specifications, the technology provides a set of built in mechanisms to support heterogeneous classes of traffic including data, voice and video, etc. Supporting heterogeneous classes of services means that the traffic is highly diverse and has distinct QoS parameters, channel and environmental conditions may vary dramatically on a short time scale. The 3GPP specifications leave unstandardized the resource management and scheduling mechanisms which are crucial components to guarantee the QoS performance for the services. In this thesis, we evaluate the performance and QoS in LTE technology. Moreover, our research addresses the resource management and scheduling issues on the wireless interface. In fact, after surveying, classifying and comparing different scheduling mechanisms, we propose three QoS mechanisms for resource allocation in macrocell scenarios focused on real time services and two mechanisms for interference mitigation in femtocell scenarios taking into account the QoS of real time services. Our first proposed mechanism for resource allocation in macrocell scenarios combines the well known virtual token (or token buckets) method with opportunistic schedulers, our second scheme utilizes game theory, specifically the Shapley value in order to achieve a higher fairness level among classes of services and our third mechanism combines the first and the second proposed schemes. Our first mechanism for interference mitigation in femtocell scenarios is power control based and works by using non cooperative games. It performs a constant bargain between throughput and SINR to find out the optimal transmit power level. The second mechanism is centralised, it uses a bandwidth division approach in order to not use the same subbands to avoid interference. The bandwidth division and assignation is performed based on game theory (Shapley value) taking into account the application bitrate . This scheme reduces interference considerably and shows an improvement compared to other bandwidth division schemes. All proposed mechanism are performed in a LTE simulation environment. several constraints such as throughput, Packet Loss Ratio, delay, fairness index, SINR are used to evaluate the efficiency of our schemesTOULOUSE-INP (315552154) / SudocSudocFranceF

Interference Aware Cognitive Femtocell Networks

Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks. Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision. This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference

Performances of LTE networks

Poussé par la demande croissante de services à haut débit sans fil, Long Term Evolution (LTE) a émergé comme une solution prometteuse pour les communications mobiles. Dans plusieurs pays à travers le monde, la mise en oeuvre de LTE est en train de se développer. LTE offre une architecture tout-IP qui fournit des débits élevés et permet une prise en charge efficace des applications de type multimédia. LTE est spécifié par le 3GPP ; cette technologie fournit une architecture capable de mettre en place des mécanismes pour traiter des classes de trafic hétérogènes comme la voix, la vidéo, les transferts de fichier, les courriers électroniques, etc. Ces classes de flux hétérogènes peuvent être gérées en fonction de la qualité de service requise mais aussi de la qualité des canaux et des conditions environnementales qui peuvent varier considérablement sur une courte échelle de temps. Les standards du 3GPP ne spécifient pas l’algorithmique de l’allocation des ressources du réseau d’accès, dont l’importance est grande pour garantir performance et qualité de service (QoS). Dans cette thèse, nous nous focalisons plus spécifiquement sur la QoS de LTE sur la voie descendante. Nous nous concentrons alors sur la gestion des ressources et l’ordonnancement sur l’interface radio des réseaux d’accès. Dans une première partie, nous nous sommes intéressés à des contextes de macro-cellules. Le premier mécanisme proposé pour l’allocation des ressources combine une méthode de jetons virtuels et des ordonnanceurs opportunistes. Les performances obtenues sont très bonnes mais n’assurent pas une très bonne équité. Notre seconde proposition repose sur la théorie des jeux, et plus spécifiquement sur la valeur de Shapley, pour atteindre un haut niveau d’équité entre les différentes classes de services au détriment de la qualité de service. Cela nous a poussé, dans un troisième mécanisme, à combiner les deux schémas. La deuxième partie de la thèse est consacrée aux femto-cellules (ou femtocells) qui offrent des compléments de couverture appréciables. La difficulté consiste alors à étudier et à minimiser les interférences. Notre premier mécanisme d’atténuation des interférences est fondé sur le contrôle de la puissance de transmission. Il fonctionne en utilisant la théorie des jeux non coopératifs. On effectue une négociation constante entre le débit et les interférences pour trouver un niveau optimal de puissance d’émission. Le second mécanisme est centralisé et utilise une approche de division de la bande passante afin d’obliger les femtocells à ne pas utiliser les mêmes sous-bandes évitant ainsi les interférences. Le partage de bande passante et l’allocation sont effectués en utilisant sur la théorie des jeux (valeur de Shapley) et en tenant compte du type d’application. Ce schéma réduit les interférences considérablement. Tous les mécanismes proposés ont été testés et évalués dans un environnement de simulation en utilisant l’outil LTE-Sim au développement duquel nous avons contribué. ABSTRACT : Driven by the growing demand for high-speed broadband wireless services, Long term Evolution (LTE) technology has emerged as a competitive alternative to mobile communications solution. In several countries around the world, the implementation of LTE has started. LTE offers an IP-based framework that provides high data rates for multimedia applications. Moreover, based on the 3GPP specifications, the technology provides a set of built in mechanisms to support heterogeneous classes of traffic including data, voice and video, etc. Supporting heterogeneous classes of services means that the traffic is highly diverse and has distinct QoS parameters, channel and environmental conditions may vary dramatically on a short time scale. The 3GPP specifications leave unstandardized the resource management and scheduling mechanisms which are crucial components to guarantee the QoS performance for the services. In this thesis, we evaluate the performance and QoS in LTE technology. Moreover, our research addresses the resource management and scheduling issues on the wireless interface. In fact, after surveying, classifying and comparing different scheduling mechanisms, we propose three QoS mechanisms for resource allocation in macrocell scenarios focused on real time services and two mechanisms for interference mitigation in femtocell scenarios taking into account the QoS of real time services. Our first proposed mechanism for resource allocation in macrocell scenarios combines the well known virtual token (or token buckets) method with opportunistic schedulers, our second scheme utilizes game theory, specifically the Shapley value in order to achieve a higher fairness level among classes of services and our third mechanism combines the first and the second proposed schemes. Our first mechanism for interference mitigation in femtocell scenarios is power control based and works by using non cooperative games. It performs a constant bargain between throughput and SINR to find out the optimal transmit power level. The second mechanism is centralised, it uses a bandwidth division approach in order to not use the same subbands to avoid interference. The bandwidth division and assignation is performed based on game theory (Shapley value) taking into account the application bitrate . This scheme reduces interference considerably and shows an improvement compared to other bandwidth division schemes. All proposed mechanism are performed in a LTE simulation environment. several constraints such as throughput, Packet Loss Ratio, delay, fairness index, SINR are used to evaluate the efficiency of our scheme

Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks

This paper considers joint power control and subchannel allocation for co-tier interference mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Speci cally, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the ef ciency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks