A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking

Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the various Joint Research Activities (JRA) in WP1.3 and the results that were developed up to the second year of the project. For each activity there is a description, an illustration of the adherence to and relevance with the identified fundamental open issues, a short presentation of the main results, and a roadmap for the future joint research. In the Annex, for each JRA, the main technical details on specific scientific activities are described in detail.Peer ReviewedPostprint (published version

Cellular and Wi-Fi technologies evolution: from complementarity to competition

This PhD thesis has the characteristic to span over a long time because while working on it, I was working as a research engineer at CTTC with highly demanding development duties. This has delayed the deposit more than I would have liked. On the other hand, this has given me the privilege of witnessing and studying how wireless technologies have been evolving over a decade from 4G to 5G and beyond. When I started my PhD thesis, IEEE and 3GPP were defining the two main wireless technologies at the time, Wi-Fi and LTE, for covering two substantially complementary market targets. Wi-Fi was designed to operate mostly indoor, in unlicensed spectrum, and was aimed to be a simple and cheap technology. Its primary technology for coexistence was based on the assumption that the spectrum on which it was operating was for free, and so it was designed with interference avoidance through the famous CSMA/CA protocol. On the other hand, 3GPP was designing technologies for licensed spectrum, a costly kind of spectrum. As a result, LTE was designed to take the best advantage of it while providing the best QoE in mainly outdoor scenarios. The PhD thesis starts in this context and evolves with these two technologies. In the first chapters, the thesis studies radio resource management solutions for standalone operation of Wi-Fi in unlicensed and LTE in licensed spectrum. We anticipated the now fundamental machine learning trend by working on machine learning-based radio resource management solutions to improve LTE and Wi-Fi operation in their respective spectrum. We pay particular attention to small cell deployments aimed at improving the spectrum efficiency in licensed spectrum, reproducing small range scenarios typical of Wi-Fi settings. IEEE and 3GPP followed evolving the technologies over the years: Wi-Fi has grown into a much more complex and sophisticated technology, incorporating the key features of cellular technologies, like HARQ, OFDMA, MU-MIMO, MAC scheduling and spatial reuse. On the other hand, since Release 13, cellular networks have also been designed for unlicensed spectrum. As a result, the two last chapters of this thesis focus on coexistence scenarios, in which LTE needs to be designed to coexist with Wi-Fi fairly, and NR, the radio access for 5G, with Wi-Fi in 5 GHz and WiGig in 60 GHz. Unlike LTE, which was adapted to operate in unlicensed spectrum, NR-U is natively designed with this feature, including its capability to operate in unlicensed in a complete standalone fashion, a fundamental new milestone for cellular. In this context, our focus of analysis changes. We consider that these two technological families are no longer targeting complementarity but are now competing, and we claim that this will be the trend for the years to come. To enable the research in these multi-RAT scenarios, another fundamental result of this PhD thesis, besides the scientific contributions, is the release of high fidelity models for LTE and NR and their coexistence with Wi-Fi and WiGig to the ns-3 open-source community. ns-3 is a popular open-source network simulator, with the characteristic to be multi-RAT and so naturally allows the evaluation of coexistence scenarios between different technologies. These models, for which I led the development, are by academic citations, the most used open-source simulation models for LTE and NR and havereceived fundings from industry (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) and federal agencies (NIST, LLNL) over the years.Aquesta tesi doctoral té la característica d’allargar-se durant un llarg període de temps ja que mentre treballava en ella, treballava com a enginyera investigadora a CTTC amb tasques de desenvolupament molt exigents. Això ha endarrerit el dipositar-la més del que m’hagués agradat. D’altra banda, això m’ha donat el privilegi de ser testimoni i estudiar com han evolucionat les tecnologies sense fils durant més d’una dècada des del 4G fins al 5G i més enllà. Quan vaig començar la tesi doctoral, IEEE i 3GPP estaven definint les dues tecnologies sense fils principals en aquell moment, Wi-Fi i LTE, que cobreixen dos objectius de mercat substancialment complementaris. Wi-Fi va ser dissenyat per funcionar principalment en interiors, en espectre sense llicència, i pretenia ser una tecnologia senzilla i barata. La seva tecnologia primària per a la convivència es basava en el supòsit que l’espectre en el que estava operant era de franc, i, per tant, es va dissenyar simplement evitant interferències a través del famós protocol CSMA/CA. D’altra banda, 3GPP estava dissenyant tecnologies per a espectres amb llicència, un tipus d’espectre costós. Com a resultat, LTE està dissenyat per treure’n el màxim profit alhora que proporciona el millor QoE en escenaris principalment a l’aire lliure. La tesi doctoral comença amb aquest context i evoluciona amb aquestes dues tecnologies. En els primers capítols, estudiem solucions de gestió de recursos de radio per a operacions en espectre de Wi-Fi sense llicència i LTE amb llicència. Hem anticipat l’actual tendència fonamental d’aprenentatge automàtic treballant solucions de gestió de recursos de radio basades en l’aprenentatge automàtic per millorar l’LTE i Wi-Fi en el seu espectre respectiu. Prestem especial atenció als desplegaments de cèl·lules petites destinades a millorar la eficiència d’espectre llicenciat, reproduint escenaris de petit abast típics de la configuració Wi-Fi. IEEE i 3GPP van seguir evolucionant les tecnologies al llarg dels anys: El Wi-Fi s’ha convertit en una tecnologia molt més complexa i sofisticada, incorporant les característiques clau de les tecnologies cel·lulars, com ara HARQ i la reutilització espacial. D’altra banda, des de la versió 13, també s’han dissenyat xarxes cel·lulars per a espectre sense llicència. Com a resultat, els dos darrers capítols d’aquesta tesi es centren en aquests escenaris de convivència, on s’ha de dissenyar LTE per conviure amb la Wi-Fi de manera justa, i NR, l’accés a la radio per a 5G amb Wi-Fi a 5 GHz i WiGig a 60 GHz. A diferència de LTE, que es va adaptar per funcionar en espectre sense llicència, NR-U està dissenyat de forma nativa amb aquesta característica, inclosa la seva capacitat per operar sense llicència de forma autònoma completa, una nova fita fonamental per al mòbil. En aquest context, el nostre focus d’anàlisi canvia. Considerem que aquestes dues famílies de tecnologia ja no estan orientades cap a la complementarietat, sinó que ara competeixen, i afirmem que aquesta serà el tendència per als propers anys. Per permetre la investigació en aquests escenaris multi-RAT, un altre resultat fonamental d’aquesta tesi doctoral, a més de les aportacions científiques, és l’alliberament de models d’alta fidelitat per a LTE i NR i la seva coexistència amb Wi-Fi a la comunitat de codi obert ns-3. ns-3 és un popular simulador de xarxa de codi obert, amb la característica de ser multi-RAT i, per tant, permet l’avaluació de manera natural d’escenaris de convivència entre diferents tecnologies. Aquests models, pels quals he liderat el desenvolupament, són per cites acadèmiques, els models de simulació de codi obert més utilitzats per a LTE i NR i que han rebut finançament de la indústria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) i agències federals (NIST, LLNL) al llarg dels anys.Esta tesis doctoral tiene la característica de extenderse durante mucho tiempo porque mientras trabajaba en ella, trabajaba como ingeniera de investigación en CTTC con tareas de desarrollo muy exigentes. Esto ha retrasado el depósito más de lo que me hubiera gustado. Por otro lado, gracias a ello, he tenido el privilegio de presenciar y estudiar como las tecnologías inalámbricas han evolucionado durante una década, de 4G a 5G y más allá. Cuando comencé mi tesis doctoral, IEEE y 3GPP estaban definiendo las dos principales tecnologías inalámbricas en ese momento, Wi-Fi y LTE, cumpliendo dos objetivos de mercado sustancialmente complementarios. Wi-Fi fue diseñado para funcionar principalmente en interiores, en un espectro sin licencia, y estaba destinado a ser una tecnología simple y barata. Su tecnología primaria para la convivencia se basaba en el supuesto en que el espectro en el que estaba operando era gratis, y así fue diseñado simplemente evitando interferencias a través del famoso protocolo CSMA/CA. Por otro lado, 3GPP estaba diseñando tecnologías para espectro con licencia, un tipo de espectro costoso. Como resultado, LTE está diseñado para aprovechar el espectro al máximo proporcionando al mismo tiempo el mejor QoE en escenarios principalmente al aire libre. La tesis doctoral parte de este contexto y evoluciona con estas dos tecnologías. En los primeros capítulos, estudiamos las soluciones de gestión de recursos de radio para operación en espectro Wi-Fi sin licencia y LTE con licencia. Anticipamos la tendencia ahora fundamental de aprendizaje automático trabajando en soluciones de gestión de recursos de radio para mejorar LTE y funcionamiento deWi-Fi en su respectivo espectro. Prestamos especial atención a las implementaciones de células pequeñas destinadas a mejorar la eficiencia de espectro licenciado, reproduciendo los típicos escenarios de rango pequeño de la configuración Wi-Fi. IEEE y 3GPP siguieron evolucionando las tecnologías a lo largo de los años: Wi-Fi se ha convertido en una tecnología mucho más compleja y sofisticada, incorporando las características clave de las tecnologías celulares, como HARQ, OFDMA, MU-MIMO, MAC scheduling y la reutilización espacial. Por otro lado, desde la Release 13, también se han diseñado redes celulares para espectro sin licencia. Como resultado, los dos últimos capítulos de esta tesis se centran en estos escenarios de convivencia, donde LTE debe diseñarse para coexistir con Wi-Fi de manera justa, y NR, el acceso por radio para 5G con Wi-Fi en 5 GHz y WiGig en 60 GHz. A diferencia de LTE, que se adaptó para operar en espectro sin licencia, NR-U está diseñado de forma nativa con esta función, incluyendo su capacidad para operar sin licencia de forma completamente independiente, un nuevo hito fundamental para los celulares. En este contexto, cambia nuestro enfoque de análisis. Consideramos que estas dos familias tecnológicas ya no tienen como objetivo la complementariedad, sino que ahora están compitiendo, y afirmamos que esta será la tendencia para los próximos años. Para permitir la investigación en estos escenarios de múltiples RAT, otro resultado fundamental de esta tesis doctoral, además de los aportes científicos, es el lanzamiento de modelos de alta fidelidad para LTE y NR y su coexistencia con Wi-Fi y WiGig a la comunidad de código abierto de ns-3. ns-3 es un simulador popular de red de código abierto, con la característica de ser multi-RAT y así, naturalmente, permite la evaluación de escenarios de convivencia entre diferentes tecnologías. Estos modelos, para los cuales lideré el desarrollo, son por citas académicas, los modelos de simulación de código abierto más utilizados para LTE y NR y han recibido fondos de la industria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) y agencias federales (NIST, LLNL) a lo largo de los años.Postprint (published version

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, need to be optimized to efficiently exploit the benefits of the architecture. However, the high computational complexity required for multi-parametric optimization of utility functions, the difficulty of modeling and solving Markov Decision Processes, the lack of guarantees of stability of Game Theory algorithms, and the rigidness of simpler methods like Cell Range Expansion and operator policies managed by the Access Network Discovery and Selection Function (ANDSF), makes neither of these state-of-the-art approaches a favorite. This Thesis proposes a framework that relies on Machine Learning techniques at the terminal device-level for Cognitive RAT Selection. The use of cognition allows the terminal device to learn both a multi-parametric state model and effective decision policies, based on the experience of the device itself. This implies that a terminal, after observing its environment during a learning period, may formulate a system characterization and optimize its own association decisions without any external intervention. In our proposal, this is achieved through clustering of appropriately defined feature vectors for building a system state model, supervised classification to obtain the current system state, and reinforcement learning for learning good policies. This Thesis describes the above framework in detail and recommends adaptations based on the experimentation with the X-means, k-Nearest Neighbors, and Q-learning algorithms, the building blocks of the solution. The network performance of the proposed framework is evaluated in a multi-agent environment implemented in MATLAB where it is compared with alternative RAT selection mechanisms

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig