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Analiza merilnih podatkov mobilnega omrežja
KOPITAR, JURE (Author), Sedlar, Urban (Mentor) More about this mentor... This link opens in a new window

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Abstract
Izvajanje nadzora v mobilnih telekomunikacijskih omrežjih ni enostavna naloga zaradi kompleksnih tehnologij in velikega števila uporabnikov. V tem zaključnem delu postavim temelje sistema za analizo merilnih podatkov mobilnega omrežja in detekcijo anomalij. Začnem z opisom nekaterih mehanizmov tehnologije LTE, ki so pomembni za visoko zmogljivost omrežja, njihova (ne)uporaba pa se odraža v merilnih rezultatih. Nadaljujem s predstavitvijo meritev v mobilnih omrežjih, kjer naštejem in opišem glavne tipe meritev in izpostavim izzive pri merjenju, na katere moramo biti pozorni. Razložim, kakšne so prednosti novega področja meritev s terminalskimi napravami in predstavim sistem qMON za merjenje v mobilnih omrežjih. V obstoječo arhitekturo qMON umestim sistem za detekcijo anomalij, predlagam njegovo arhitekturo in razložim, zakaj je sistem za detekcijo anomalij končni cilj raziskovalnega dela. Nadaljujem z opisom praktičnega dela, tj. izvedbo štirih merilnih poskusov. Vsakemu merilnemu poskusu postavim hipotezo, katere veljavnosti se bo treba zavedati pri implementaciji sistema za detekcijo anomalij. Hipoteze preverim z analizo merilnih rezultatov v programskem okolju Python z modulom pandas. V začetnem delu analize na primeru izbire podmnožice atributov pokažem, da se je v domeni mobilnih omrežij težko izogniti potrebi po ekspertnem znanju. Analizo merilnih rezultatov predstavim kot problem večrazsežnih neenakomerno vzorčenih časovnih vrst, ki ga pretvorim na problem enakomerno vzorčenih časovnih vrst. Na pretvorjenih podatkih preverim veljavnost hipotez s poudarkom na izmerjeni podatkovni prepustnosti in kvaliteti radijskega signala (RSRQ), ter njunem medsebojnem odnosu. Ugotovim, da podatki vsebujejo močno 24-urno sezonsko komponento in da smo v merilnih rezultatih sposobni zaznati vpliv stresnega testa bazne postaje. V nadaljevanju ugotovim tudi, da na podlagi meritev RSRQ ne moremo dobro oceniti števila aktivnih naprav v celici. Pokažem še, da moramo biti pri modeliranju pozorni na soobstoj različnih tehnologij mobilnih komunikacij; značilnosti merilnih rezultatov tehnologij LTE in UMTS so namreč različne. V zaključku ocenim, da smo se s pričujočo analizo delujočemu sistemu za detekcijo anomalij približali do faze modeliranja.

Language:Slovenian
Keywords:LTE, mobilno omrežje, meritve, merilni sistem, detekcija anomalij, RSRQ, podatkovna prepustnost, iperf, Python
Work type:Master's thesis/paper (mb22)
Organization:FE - Faculty of Electrical Engineering
Year:2019
Views:257
Downloads:155
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Secondary language

Language:English
Title:Analysis of mobile network measurement data
Abstract:
Mobile network monitoring is not a trivial task to perform. The complexity originates mainly from the usage of complex technologies and a large number of users connected to the network. This thesis deals with setting a baseline for mobile network measurement data analysis and anomaly detection. Firstly, a few significant mechanisms of LTE technology are presented. The usage of these mechanisms is important for providing high data throughput and is reflected in measurement results. I continue with the description of various types of measurements in mobile networks. In addition to this, key challenges that should be considered when performing these measurements are presented. Furthermore, a new field of measurements with user-equipment (i.e. mobile phones) acting as a measurement device is introduced. qMON is a measurement system for mobile networks which enables user-equipment measurements. I present the existing qMON architecture and explain how an anomaly detection system can be added to it. Four experiments were performed using the qMON system and a hypothesis was formed for each of the experiments. I explain, why each of the hypotheses is important for a successful implementation of anomaly detection system. Analysis of measurement results is performed with Python in order to test the hypotheses. Initial part of data analylisis is carried out with the intention of showing the importance of expert knowledge in mobile network analysis. This importance is demonstrated through a feature subset selection process which is followed by the definition of the problem – we are dealing with multivariate and unevenly sampled time series. I explain the procedure which was used in order to convert the problem to an evenly sampled time series problem. The converted data, which is the result of the aforementioned procedure, is used to test the hypotheses. In the testing phase I focus on a subset of attributes, namely the quality of the received reference radio signal (RSRQ) and the measured network data throughput. A significant 24-hour seasonal component is discovered in the data, as well as the effect that the stress test of the base station has on the measurement results. I also show that the number of active devices in a cell cannot be assessed through RSRQ measurements. Key differences between LTE and UMTS technologies are presented as the final analysis result. The thesis is concluded with an estimate of how close we are to a functioning anomaly detection system.

Keywords:LTE, mobile network, measurements, measurement system, anomaly detection, RSRQ, data throughput, iperf, Python

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