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

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Energy-aware routing techniques for software-defined networks

Achieving energy efficiency has recently become a key topic of networking research due to the ever-increasing power consumption and CO2 emissions generated by large data networks. This problem is becoming even more concerning and challenging given the drastic traffic increase expected over the next few years. However, the use of efficient energy-aware strategies could overturn this situation reducing the electricity consumption of Internet data transmission networks, as well as contributing to mitigate the environmental impact of other sectors. The existence of redundant network elements with high capacities is a common design practice in current network infrastructures in order to face suddenly failures or peak traffic flows. However, these additional resources remain either unused or barely used most of the time leading to an undesired energy waste. Therefore, putting into sleep mode (i.e. a low-power state) unused elements is an effective and widely-accepted strategy to decrease the consumption of data networks. In this context, SDN can be seen as an attractive solution to achieve the long-awaited energy efficiency in current communications systems, since they allow a flexible programmability suitable for this problem. This doctoral thesis tackles the problem of optimizing the power consumption in SDN through the design of energy-aware routing techniques that minimize the number of network elements required to satisfy an incoming traffic load. Different from existing related works, we focus on optimizing energy consumption in SDN with in-band control traffic in order to close this important gap in the literature and provide solutions compatible with operational backbone networks. Complementing the general aim of improving the energy efficiency in SDN, this research is also intended to cover important related features such as network performance, QoS requirements and real-time operation. Accordingly, this study gives a general perspective about the use of energy efficient routing techniques, which cover integrated routing considerations for the data and control plane traffic in SDN. By using realistic input data, significant values of switched-off links and nodes are reached, which demonstrates the great opportunity for saving energy given by our proposals. The obtained results have also validated the intrinsic trade-off between environmental and performance concerns, considering several performance indicators. These findings confirm that energy-aware routing schemes should be designed considering specific traffic requirements and performance metric bounds. Moreover, it is shown that jointly considering QoS requirements and energy awareness is an effective approach to improve, not only the power consumption, but the performance on critical parameters such as control traffic delay and blocking rate. Similarly, the proposed dynamic traffic allocation with congestion-aware rerouting is able to handle demanding traffic arrival without degrading the performance of higher priority traffic. In general, our proposals are fine-grained, easy to implement and quite balanced and effective in their results looking for a suitable and readily deployment in real-world SDN scenarios. Therefore, the conducted research and contributions reported through this document not only add to what is known about the potential of energy-aware routing techniques, but also stand as a valuable solution on the road to a sustainable networking.L'assoliment de l'eficiència energètica s'ha convertit recentment en un tema clau de recerca de xarxes a causa dels creixents nivells de consum d'energia i emissions de CO2 generats per les xarxes de dades. Aquest problema es torna cada vegada més preocupant i desafiant, donat el dràstic augment del trànsit esperat en els propers anys. No obstant això, l'ús d'estratègies energètiques eficients podria invertir aquesta situació, reduint el consum d'electricitat de les xarxes de dades d'Internet i contribuint a mitigar l'impacte ambiental d'altres sectors. L'existència d'elements de xarxa redundants i amb grans capacitats és una pràctica de disseny habitual en les infraestructures de xarxes actuals per afrontar fallades sobtades o fluxos de trànsit més elevats. Tanmateix, aquests recursos addicionals romanen poc o gens utilitzats la major part del temps, generant un desaprofitament d'energia no desitjat. Per tant, posar en mode de repòs (és a dir, un estat de baixa potència) elements no utilitzats és una estratègia efectiva i àmpliament acceptada per disminuir el consum en xarxes de dades. En aquest context, les xarxes definides per programari (SDN) es poden considerar una solució atractiva per aconseguir l'esperada eficiència energètica en els sistemes de comunicacions actuals, ja que permeten una flexible programabilitat idònia per a aquest problema. Aquesta tesi doctoral aborda el problema d'optimitzar el consum d'energia en SDN a través del disseny de tècniques d'encaminament conscients de l'energia que minimitzen la quantitat d'elements de xarxa necessaris per satisfer una càrrega de trànsit entrant. Diferent dels treballs existents, aquesta tesi es centra a optimitzar el consum d'energia en SDN amb el control de tràfic dins de banda per tancar aquesta important bretxa en la literatura i proporcionar solucions compatibles amb xarxes troncals operatives. Complementant l'objectiu general de millorar l'eficiència energètica en SDN, aquesta recerca també pretén cobrir altres importants paràmetres relacionats, com ara el rendiment de la xarxa, els requisits de qualitat de servei (QoS) i el funcionament en temps real. En conseqüència, aquest estudi ofereix una perspectiva general sobre l'ús de tècniques d'encaminament eficients energèticament, que contempla consideracions integrades per al tràfic de dades i del pla de control en SDN. Prenent dades d'entrada realistes, es van aconseguir desconnectar significatives quantitats d'enllaços i nodes, la qual cosa demostra la gran oportunitat d'estalvi d'energia que ofereixen les nostres propostes. Els resultats obtinguts també validen el estret compromís entre les preocupacions ambientals i les qüestions de rendiment de la xarxa, considerant diversos indicadors de rendiment. Aquests resultats confirmen que els esquemes d'encaminament conscients de l'energia s'han de dissenyar tenint en compte els requisits de tràfic específics i els límits desitjats de les mètriques de rendiment. A més, es demostra que, considerant conjuntament els requisits de QoS i de l'energia necessària, és un enfocament eficaç per millorar, no només el consum d'energia, sinó també el rendiment en paràmetres crítics, com la latència del tràfic de control i la probabilitat de bloqueig. De manera semblant, l'assignació dinàmica de tràfic proposta, amb re-encaminament conscient de la congestió, permet gestionar grans volums de trànsit sense degradar el rendiment de les demandes de major prioritat. En general, les nostres propostes són precises, fàcils d'implementar i bastant equilibrades i efectives en els seus resultats, buscant un desplegament adequat i fàcil en escenaris pràctics de SDN. Per tant, la recerca realitzada i les contribucions contingudes en aquest document no només afegeixen el que es coneix sobre el potencial de les tècniques d'encaminament conscients de l'energia, sinó que també representen una valuosa solució en el camí cap a una xarxa sostenibl

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

Novel Resource and Energy Management for 5G Integrated Backhaul/Fronthaul (5G-Crosshaul)

The integration of both fronthaul and backhaul into a single transport network (namely, 5G-Crosshaul) is envisioned for the future 5G transport networks. This requires a fully integrated and unified management of the fronthaul and backhaul resources in a cost-efficient, scalable and flexible way through the deployment of an SDN/NFV control framework. This paper presents the designed 5G-Crosshaul architecture, two selected SDN/NFV applications targeting for cost-efficient resource and energy usage: the Resource Management Application (RMA) and the Energy Management and Monitoring Application (EMMA). The former manages 5G-Crosshaul resources (network, computing and storage resources). The latter is a special version of RMA with the focus on the objectives of optimizing the energy consumption and minimizing the energy footprint of the 5G-Crosshaul infrastructure. Besides, EMMA is applied to the mmWave mesh network and the high speed train scenarios. In particular, we present the key application design with their main components and the interactions with each other and with the control plane, and then we present the proposed application optimization algorithms along with initial results. The first results demonstrate that the proposed RMA is able to cost-efficiently utilize the Crosshaul resources of heterogeneous technologies, while EMMA can achieve significant energy savings through energy-efficient routing of traffic flows. For experiments in real system, we also set up Proof of Concepts (PoCs) for both applications in order to perform real trials in the field.© 2017 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

Wearable Communications in 5G: Challenges and Enabling Technologies

As wearable devices become more ingrained in our daily lives, traditional communication networks primarily designed for human being-oriented applications are facing tremendous challenges. The upcoming 5G wireless system aims to support unprecedented high capacity, low latency, and massive connectivity. In this article, we evaluate key challenges in wearable communications. A cloud/edge communication architecture that integrates the cloud radio access network, software defined network, device to device communications, and cloud/edge technologies is presented. Computation offloading enabled by this multi-layer communications architecture can offload computation-excessive and latency-stringent applications to nearby devices through device to device communications or to nearby edge nodes through cellular or other wireless technologies. Critical issues faced by wearable communications such as short battery life, limited computing capability, and stringent latency can be greatly alleviated by this cloud/edge architecture. Together with the presented architecture, current transmission and networking technologies, including non-orthogonal multiple access, mobile edge computing, and energy harvesting, can greatly enhance the performance of wearable communication in terms of spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin