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

Analysis and Ad-hoc Networking Solutions for Cooperative Relaying Systems

Users of mobile networks are increasingly demanding higher data rates from their service providers. To cater to this demand, various signal processing and networking algorithms have been proposed. Amongst them the multiple input multiple output (MIMO) scheme of wireless communications is one of the most promising options. However, due to certain physical restrictions, e.g., size, it is not possible for many devices to have multiple antennas on them. Also, most of the devices currently in use are single-antenna devices. Such devices can make use of the MIMO scheme by employing cooperative MIMO methods. This involves nearby nodes utilizing the antennas of each other to form virtual antenna arrays (VAAs). Nodes with limited communication ranges can further employ multi-hopping to be able to communicate with far away nodes. However, an ad-hoc communications scheme with cooperative MIMO multi-hopping can be challenging to implement because of its de-centralized nature and lack of a centralized controling entity such as a base-station. This thesis looks at methods to alleviate the problems faced by such networks.In the first part of this thesis, we look, analytically, at the relaying scheme under consideration and derive closed form expressions for certain performance measures (signal to noise ratio (SNR), symbol error rate (SER), bit error rate (BER), and capacity) for the co-located and cooperative multiple antenna schemes in different relaying configurations (amplify-and-forward and decode-and-forward) and different antenna configurations (single input single output (SISO), single input multiple output (SIMO) and MIMO). These expressions show the importance of reducing the number of hops in multi-hop communications to achieve a better performance. We can also see the impact of different antenna configurations and different transmit powers on the number of hops through these simplified expressions.We also look at the impact of synchronization errors on the cooperative MIMO communications scheme and derive a lower bound of the SINR and an expression for the BER in the high SNR regime. These expressions can help the network designers to ensure that the quality of service (QoS) is satisfied even in the worst-case scenarios. In the second part of the thesis we present some algorithms developed by us to help the set-up and functioning of cluster-based ad-hoc networks that employ cooperative relaying. We present a clustering algorithm that takes into account the battery status of nodes in order to ensure a longer network life-time. We also present a routing mechanism that is tailored for use in cooperative MIMO multi-hop relaying. The benefits of both schemes are shown through simulations.A method to handle data in ad-hoc networks using distributed hash tables (DHTs) is also presented. Moreover, we also present a physical layer security mechanism for multi-hop relaying. We also analyze the physical layer security mechanism for the cooperative MIMO scheme. This analysis shows that the cooperative MIMO scheme is more beneficial than co-located MIMO in terms of the information theoretic limits of the physical layer security.Nutzer mobiler Netzwerke fordern zunehmend höhere Datenraten von ihren Dienstleistern. Um diesem Bedarf gerecht zu werden, wurden verschiedene Signalverarbeitungsalgorithmen entwickelt. Dabei ist das "Multiple input multiple output" (MIMO)-Verfahren für die drahtlose Kommunikation eine der vielversprechendsten Techniken. Jedoch ist aufgrund bestimmter physikalischer Beschränkungen, wie zum Beispiel die Baugröße, die Verwendung von mehreren Antennen für viele Endgeräte nicht möglich. Dennoch können solche Ein-Antennen-Geräte durch den Einsatz kooperativer MIMO-Verfahren von den Vorteilen des MIMO-Prinzips profitieren. Dabei schließen sich naheliegende Knoten zusammen um ein sogenanntes virtuelles Antennen-Array zu bilden. Weiterhin können Knoten mit beschränktem Kommunikationsbereich durch mehrere Hops mit weiter entfernten Knoten kommunizieren. Allerdings stellt der Aufbau eines solchen Ad-hoc-Netzwerks mit kooperativen MIMO-Fähigkeiten aufgrund der dezentralen Natur und das Fehlen einer zentral-steuernden Einheit, wie einer Basisstation, eine große Herausforderung dar. Diese Arbeit befasst sich mit den Problemstellungen dieser Netzwerke und bietet verschiedene Lösungsansätze.Im ersten Teil dieser Arbeit werden analytisch in sich geschlossene Ausdrücke für ein kooperatives Relaying-System bezüglicher verschiedener Metriken, wie das Signal-Rausch-Verhältnis, die Symbolfehlerrate, die Bitfehlerrate und die Kapazität, hergeleitet. Dabei werden die "Amplify-and forward" und "Decode-and-forward" Relaying-Protokolle, sowie unterschiedliche Mehrantennen-Konfigurationen, wie "Single input single output" (SISO), "Single input multiple output" (SIMO) und MIMO betrachtet. Diese Ausdrücke zeigen die Bedeutung der Reduzierung der Hop-Anzahl in Mehr-Hop-Systemen, um eine höhere Leistung zu erzielen. Zudem werden die Auswirkungen verschiedener Antennen-Konfigurationen und Sendeleistungen auf die Anzahl der Hops analysiert.  Weiterhin wird der Einfluss von Synchronisationsfehlern auf das kooperative MIMO-Verfahren herausgestellt und daraus eine untere Grenze für das Signal-zu-Interferenz-und-Rausch-Verhältnis, sowie ein Ausdruck für die Bitfehlerrate bei hohem Signal-Rausch-Verhältnis entwickelt. Diese Zusammenhänge sollen Netzwerk-Designern helfen die Qualität des Services auch in den Worst-Case-Szenarien sicherzustellen. Im zweiten Teil der Arbeit werden einige innovative Algorithmen vorgestellt, die die Einrichtung und die Funktionsweise von Cluster-basierten Ad-hoc-Netzwerken, die kooperative Relays verwenden, erleichtern und verbessern. Darunter befinden sich ein Clustering-Algorithmus, der den Batteriestatus der Knoten berücksichtigt, um eine längere Lebensdauer des Netzwerks zu gewährleisten und ein Routing-Mechanismus, der auf den Einsatz in kooperativen MIMO Mehr-Hop-Systemen zugeschnitten ist. Die Vorteile beider Algorithmen werden durch Simulationen veranschaulicht. Eine Methode, die Daten in Ad-hoc-Netzwerken mit verteilten Hash-Tabellen behandelt wird ebenfalls vorgestellt. Darüber hinaus wird auch ein Sicherheitsmechanismus für die physikalische Schicht in Multi-Hop-Systemen und kooperativen MIMO-Systemen präsentiert. Eine Analyse zeigt, dass das kooperative MIMO-Verfahren deutliche Vorteile gegenüber dem konventionellen MIMO-Verfahren hinsichtlich der informationstheoretischen Grenzen der Sicherheit auf der physikalischen Schicht aufweist

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

Mobile Broadband Possibilities considering the Arrival of IEEE 802.16m & LTE with an Emphasis on South Asia

This paper intends to look deeper into finding an ideal mobile broadband solution. Special stress has been put in the South Asian region through some comparative analysis. Proving their competency in numerous aspects, WiMAX and LTE already have already made a strong position in telecommunication industry. Both WiMAX and LTE are 4G technologies designed to move data rather than voice having IP networks based on OFDM technology. So, they aren't like typical technological rivals as of GSM and CDMA. But still a gesture of hostility seems to outburst long before the stable commercial launch of LTE. In this paper various aspects of WiMAX and LTE for deployment have been analyzed. Again, we tried to make every possible consideration with respect to south Asia i.e. how mass people of this region may be benefited. As a result, it might be regarded as a good source in case of making major BWA deployment decisions in this region. Besides these, it also opens the path for further research and in depth thinking in this issue.Comment: IEEE Publication format, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Wireless mesh networks for smart-grids

Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores - Major Telecomunicações. Faculdade de Engenharia. Universidade do Porto. 201