240 research outputs found
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
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
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 tĂ© la caracterĂstica d’allargar-se durant un llarg perĂode 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 mĂ©s del que m’haguĂ©s agradat. D’altra banda, això m’ha donat el privilegi de ser testimoni i estudiar com han evolucionat les tecnologies sense fils durant mĂ©s d’una dècada des del 4G fins al 5G i mĂ©s enllĂ . 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 primĂ ria 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 travĂ©s del famĂłs protocol CSMA/CA. D’altra banda, 3GPP estava dissenyant tecnologies per a espectres amb llicència, un tipus d’espectre costĂłs. Com a resultat, LTE estĂ dissenyat per treure’n el mĂ xim 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 capĂtols, estudiem solucions de gestiĂł 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 automĂ tic treballant solucions de gestiĂł de recursos de radio basades en l’aprenentatge automĂ tic per millorar l’LTE i Wi-Fi en el seu espectre respectiu. Prestem especial atenciĂł als desplegaments de cèl·lules petites destinades a millorar la eficiència d’espectre llicenciat, reproduint escenaris de petit abast tĂpics de la configuraciĂł Wi-Fi. IEEE i 3GPP van seguir evolucionant les tecnologies al llarg dels anys: El Wi-Fi s’ha convertit en una tecnologia molt mĂ©s complexa i sofisticada, incorporant les caracterĂstiques clau de les tecnologies cel·lulars, com ara HARQ i la reutilitzaciĂł espacial. D’altra banda, des de la versiĂł 13, tambĂ© s’han dissenyat xarxes cel·lulars per a espectre sense llicència. Com a resultat, els dos darrers capĂtols 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’accĂ©s 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 estĂ dissenyat de forma nativa amb aquesta caracterĂstica, 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’anĂ lisi canvia. Considerem que aquestes dues famĂlies de tecnologia ja no estan orientades cap a la complementarietat, sinĂł que ara competeixen, i afirmem que aquesta serĂ el tendència per als propers anys. Per permetre la investigaciĂł en aquests escenaris multi-RAT, un altre resultat fonamental d’aquesta tesi doctoral, a mĂ©s de les aportacions cientĂfiques, Ă©s 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 Ă©s un popular simulador de xarxa de codi obert, amb la caracterĂstica de ser multi-RAT i, per tant, permet l’avaluaciĂł de manera natural d’escenaris de convivència entre diferents tecnologies. Aquests models, pels quals he liderat el desenvolupament, sĂłn per cites acadèmiques, els models de simulaciĂł de codi obert mĂ©s utilitzats per a LTE i NR i que han rebut finançament de la indĂşstria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) i agències federals (NIST, LLNL) al llarg dels anys.Esta tesis doctoral tiene la caracterĂstica de extenderse durante mucho tiempo porque mientras trabajaba en ella, trabajaba como ingeniera de investigaciĂłn en CTTC con tareas de desarrollo muy exigentes. Esto ha retrasado el depĂłsito más de lo que me hubiera gustado. Por otro lado,
gracias a ello, he tenido el privilegio de presenciar y estudiar como las tecnologĂas 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
tecnologĂas 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 tecnologĂa simple y barata.
Su tecnologĂa primaria para la convivencia se basaba en el supuesto en que el espectro en
el que estaba operando era gratis, y asà fue diseñado simplemente evitando interferencias a
travĂ©s del famoso protocolo CSMA/CA. Por otro lado, 3GPP estaba diseñando tecnologĂas
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 tecnologĂas. En los
primeros capĂtulos, estudiamos las soluciones de gestiĂłn de recursos de radio para operaciĂłn
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 atenciĂłn
a las implementaciones de células pequeñas destinadas a mejorar la eficiencia de espectro
licenciado, reproduciendo los tĂpicos escenarios de rango pequeño de la configuraciĂłn Wi-Fi.
IEEE y 3GPP siguieron evolucionando las tecnologĂas a lo largo de los años: Wi-Fi
se ha convertido en una tecnologĂa mucho más compleja y sofisticada, incorporando las
caracterĂsticas clave de las tecnologĂas 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 Ăşltimos capĂtulos
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 adaptĂł 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 investigaciĂłn en estos escenarios de mĂşltiples RAT, otro resultado fundamental
de esta tesis doctoral, además de los aportes cientĂficos, es el lanzamiento de modelos de alta
fidelidad para LTE y NR y su coexistencia con Wi-Fi y WiGig a la comunidad de cĂłdigo
abierto de ns-3. ns-3 es un simulador popular de red de cĂłdigo abierto, con la caracterĂstica
de ser multi-RAT y asĂ, naturalmente, permite la evaluaciĂłn de escenarios de convivencia
entre diferentes tecnologĂas. Estos modelos, para los cuales liderĂ© 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
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
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks
Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN
Accelerating Reinforcement Learning for Dynamic Spectrum Access in Cognitive Wireless Networks
This thesis studies the applications of distributed reinforcement learning (RL) based machine intelligence to dynamic spectrum access (DSA) in future cognitive wireless networks. In particular, this work focuses on ways of accelerating distributed RL based DSA algorithms in order to improve their adaptability in terms of the initial and steady-state performance, and the quality of service (QoS) convergence behaviour. The performance of the DSA schemes proposed in this thesis is empirically evaluated using large-scale system-level simulations of a temporary event scenario which involves a cognitive small cell network installed in a densely populated stadium, and in some cases a base station on an aerial platform and a number of local primary LTE base stations, all sharing the same spectrum. Some of the algorithms are also theoretically evaluated using a Bayesian network based probabilistic convergence analysis method proposed by the author.
The thesis presents novel distributed RL based DSA algorithms that employ a Win-or-Learn-Fast (WoLF) variable learning rate and an adaptation of the heuristically accelerated RL (HARL) framework in order to significantly improve the initial performance and the convergence speed of classical RL algorithms and, thus, increase their adaptability in challenging DSA environments. Furthermore, a distributed case-based RL approach to DSA is proposed. It combines RL and case-based reasoning to increase the robustness and adaptability of distributed RL based DSA schemes in dynamically changing wireless environments
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