7 research outputs found
Telecommunications Wireless Generations: Overview, Technological Differences, Evolutional Triggers, and the Future
This study expands on prior studies on wireless telecommunication generations by examining the technological differences and evolutional triggers that characterise each Generation (from 1G to 5G). Based on a systematic literature review approach, this study examines fifty (50) articles to enhance our understanding of wireless generation evolution. Specifically, this study analyses i) the triggers that necessitated the evolution of wireless telecommunication generations and ii) makes a case regarding why it is imperative to look beyond the fifth Generation (5G) network technologies. The authors propose areas for future research
Algoritmos de aprendizado de máquina para coordenação de interferência entre células
The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.Los despliegues actuales de LTE y LTE-A requieren mayor esfuerzo para la gestión de recursos radio debido al incremento de usuarios y a la gran demanda de servicios; en ese escenario, la optimización automática es un punto clave para evitar problemas como la interferencia inter-celda. El presente trabajo recopila propuestas de algoritmos de aprendizaje automático [machine learning] enfocados en resolver este problema. Las investigaciones buscan que los sistemas celulares consigan su auto-optimización, un concepto que se enmarca dentro del área de redes auto-organizadas [Self-Organized Networks, SON], cuyo objetivo es lograr que las redes respondan de forma automática a las necesidades de los escenarios dinámicos de tráfico de red.As implantações atuais de LTE e LTE-A exigem maior esforço para o gerenciamento de recursos rádio devido ao aumento de usuários e à alta demanda por serviços, neste cenário a otimização automática é um ponto-chave para evitar problemas como a interferência entre células. O presente trabalho coleta propostas de algoritmos de aprendizado automáticos focados na resolução deste problema. A pesquisa busca que os sistemas celulares alcancem a sua auto-otimização, um conceito que faz parte das redes auto-organizadas (Self-Organizing Networks, SON), cujo objetivo é garantir que as redes respondam automaticamente às necessidades dos cenários dinâmicos do tráfego de rede
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
Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks
Nutzer der Mobilfunknetze wünschen und fordern eine Steigerung des
Datendurchsatzes, die zur Erhöhung der Netzlast führt. Besonders seit der
Einführung von LTE erhöht sich daher die Anzahl und Dichte der Zellen in
Mobilfunknetzen. Dies führt zusätzlich zur Zunahme der Investitions- und
Betriebskosten, sowie einer höheren Komplexität des Nerzbetriebs. Der
Einsatz selbstorganisierter Netze (SONs) wird vorgeschlagen, um diese drei
Herausforderungen zu bewältigen. Einige SON-Funktionen (SF) wurden sowohl
von Seiten der Netzbetreiber als auch von den Standardisierungsgremien
vorgeschlagen. Eine SF repräsentiert hierbei eine Netzfunktion, die
automatisiert werden kann. Ein Beispiel ist die Optimierung der Robustheit
des Netzes (Mobility Robustness Optimization, MRO) oder der Lastausgleich
zwischen Funkzellen (Mobility Load Balancing, MLB).
Die unterschiedlichen SON-Funktionen werden innerhalb eines Mobilfunknetzes
eingesetzt, wobei sie dabei häufig gleiche oder voneinander abhängige
Parameter optimieren. Zwangsläufig treten daher beim Einsatz paralleler
SON-Funktionen Konflikte auf, die Mechanismen erfordern, um diese
Konflikte aufzulösen oder zu minimieren. In dieser Dissertation werden
Lösungen aufgezeigt und untersucht, um die Koordination der SON-Funktionen
zu automatisieren und, soweit möglich, gleichmä{\ss}ig zu verteilen.
Im ersten Teil werden grundsätzliche Entwürfe für SFs evaluiert, um die
SON-Koordination zu vereinfachen. Basierend auf der Beobachtung, dass die
Steurung der SON-Funktion sich ähnlich dem generischen Q-Learning Problem
verhält, werden die SFs als Q-Learning-Agenten entworfen. Dieser Ansatz
wurde mit sehr positiven Ergebnissen auf zwei SFs (MRO und MLB) angewandt.
Die als Q-Learning-Agenten entworfenen SFs werden für zwei
unterschiedliche Ansätze der SON-Koordination evaluiert. Beide
Koordinierungsansätze betrachten dabei die SON-Umgebung als ein
Multi-Agenten-System. Der erste Ansatz basierend auf einer
räumlich-zeitlichen Entkoppelung separiert die Ausführung von
SF-Instanzen sowohl räumlich als auch zeitlich, um die Konflikte zwischen
den SF-Instanzen zu minimieren. Der zweite Ansatz wendet kooperatives
Lernen in Multi-Agenten-Systemen als automatisierten Lösungsansatz zur
SON-Koordination an. Die einzelnen SF-Instanzen lernen anhand von
Utility-Werten, die sowohl die eigenen Metriken als auch die Metriken der
Peer-SF-Instanzen auswerten. Die Intention dabei ist, durch die erlernte
Zustands-Aktions-Strategie Aktionen auszuführen, die das beste Resultat
für die aktive SF, aber auch die geringste Auswirkung auf Peer-SFs
gewährleisten. In der Evaluation des MRO-MLB-Konflikts zeigten beide
Koordinierungsansätze sehr gute Resultate.Owing to increase in desired user throughput and to the subsequent increase
in network traffic, the number and density of cells in cellular networks
have increased, especially starting with LTE. This directly translates into
higher capital and operational expenses as well as increased complexity of
network operation. To counter all three challenges, Self-Organized
Networks (SON) have been proposed. A number of SON Functions (SFs) have
been defined both from the network operator community as well as from the
standardization bodies. In this respect, a SF represents a network
function that can be automated e.g. Mobility Robustness Optimization (MRO)
or Mobility Load balancing (MLB).
The different SFs operate on the same radio network, in many cases
adjusting the same or related parameters. Conflicts are as such bound to
occur during the parallel operation of such SFs and mechanisms are required
to resolve or minimize the conflicts. This thesis studies the solutions
through which SON functions can be coordinated in an automated and
preferably distributed manner.
In the first part we evaluate the design principles of SFs that aim at
easing the coordination. With the observation that the SON control loop is
similar to a generic Q-learning problem, we propose designing SFs as
Q-learning agents. This framework is applied to two SFs (MRO and MLB) with
very positive results. Given the designed QL based SFs, we then
evaluate two SON coordination approaches that consider the SON environment
as a Multi-Agent System (MAS). The first approach based on
Spatial-Temporal Decoupling (STD) separates the execution of SF instances
in space and time so as to minimize the conflicts among instances. The
second approach applies multi-agent cooperative learning for an automated
solution towards SON coordination. In this case individual SF instances
learn based on utilities that aggregate their own metrics as well as the
metrics of peer SF instances. The intention in this case is to ensure that
the learned state-action policy functions apply actions that guarantee the
best result for the active SF but also have the least effect on the peer
SFs. Both coordination approaches have been evaluated with very positive
results in simulations that consider the MRO - MLB conflict