1,403 research outputs found
A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters
Current LTE network is faced with a plethora of Configuration and
Optimization Parameters (COPs), both hard and soft, that are adjusted manually
to manage the network and provide better Quality of Experience (QoE). With 5G
in view, the number of these COPs are expected to reach 2000 per site, making
their manual tuning for finding the optimal combination of these parameters, an
impossible fleet. Alongside these thousands of COPs is the anticipated network
densification in emerging networks which exacerbates the burden of the network
operators in managing and optimizing the network. Hence, we propose a machine
learning-based framework combined with a heuristic technique to discover the
optimal combination of two pertinent COPs used in mobility, Cell Individual
Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key
Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio
(SINR) of all the connected users. The first part of the framework leverages
the power of machine learning to predict the KPI of interest given several
different combinations of CIO and HOM. The resulting predictions are then fed
into Genetic Algorithm (GA) which searches for the best combination of the two
mentioned parameters that yield the maximum mean SINR for all users.
Performance of the framework is also evaluated using several machine learning
techniques, with CatBoost algorithm yielding the best prediction performance.
Meanwhile, GA is able to reveal the optimal parameter setting combination more
efficiently and with three orders of magnitude faster convergence time in
comparison to brute force approach
Mecanismos para controlo e gestão de redes 5G: redes de operador
In 5G networks, time-series data will be omnipresent for the monitoring of network
metrics. With the increase in the number of Internet of Things (IoT) devices
in the next years, it is expected that the number of real-time time-series
data streams increases at a fast pace. To be able to monitor those streams,
test and correlate different algorithms and metrics simultaneously and in a
seamless way, time-series forecasting is becoming essential for the pro-active
successful management of the network.
The objective of this dissertation is to design, implement and test a prediction
system in a communication network, that allows integrating various networks,
such as a vehicular network and a 4G operator network, to improve the network
reliability and Quality-of-Service (QoS). To do that, the dissertation has
three main goals: (1) the analysis of different network datasets and implementation
of different approaches to forecast network metrics, to test different
techniques; (2) the design and implementation of a real-time distributed
time-series forecasting architecture, to enable the network operator to make
predictions about the network metrics; and lastly, (3) to use the forecasting
models made previously and apply them to improve the network performance
using resource management policies.
The tests done with two different datasets, addressing the use cases of congestion
management and resource splitting in a network with a limited number
of resources, show that the network performance can be improved with proactive
management made by a real-time system able to predict the network
metrics and act on the network accordingly.
It is also done a study about what network metrics can cause reduced accessibility
in 4G networks, for the network operator to act more efficiently and
pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização
de métricas de rede. Com o aumento do número de dispositivos da Internet
das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de
séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar
esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente
e de maneira integrada, a previsão de séries temporais está a
tornar-se essencial para a gestão preventiva bem sucedida da rede.
O objetivo desta dissertação é desenhar, implementar e testar um sistema
de previsão numa rede de comunicações, que permite integrar várias redes
diferentes, como por exemplo uma rede veicular e uma rede 4G de operador,
para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso,
a dissertação tem três objetivos principais: (1) a análise de diferentes datasets
de rede e subsequente implementação de diferentes abordagens para
previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho
e implementação de uma arquitetura distribuÃda de previsão de séries temporais
em tempo real, para permitir ao operador de rede efetuar previsões
sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão
criados anteriormente e sua aplicação para melhorar o desempenho da rede
utilizando polÃticas de gestão de recursos.
Os testes efetuados com dois datasets diferentes, endereçando os casos de
uso de gestão de congestionamento e divisão de recursos numa rede com
recursos limitados, mostram que o desempenho da rede pode ser melhorado
com gestão preventiva da rede efetuada por um sistema em tempo real capaz
de prever métricas de rede e atuar em conformidade na rede.
Também é efetuado um estudo sobre que métricas de rede podem causar
reduzida acessibilidade em redes 4G, para o operador de rede atuar mais
eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic
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
Quadri-dimensional approach for data analytics in mobile networks
The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data.
While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation.
The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering
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