phd thesis seminar presentation 2 - jyväskylän...

UNIVERSITY OF JYVÄSKYLÄ

PhD Thesis Seminar

Presentation 2

Fedor Chernogorov

17.04.2013


Structure of Presentation

Introduction and Background

Research problem description

Example of research work – conference paper

presentation

2


Introduction

Scientific advisor: Dr. Prof. Tapani Ristaniemi

Thesis format: collection of articles

Working title: Enhanced Performance Monitoring and

Self-Organization for Future Mobile Networks

3


Main Scope of PhD

Research work is focused on improvement of

operational performance in Long Term Evolution

(LTE) mobile networks.

This is closely related to ongoing 3GPP* LTE

standardization, and specifically to Self-Organizing

Networks

4

* 3GPP – 3rd Generation Partnership Project


BACKGROUND

Self-Organizing Networks, Minimization of Drive Tests

5


Self-Organizing Networks (SON)

SON is a large area of LTE standardization devoted to

automation of routine tasks in cellular networks.

Main goal is to reduce expenses, increase reliability and

improve quality.

In 3GPP focus is on more simple automation algorithms

for different SON tasks

6


Self-Organizing Networks (SON)

Self-Configuration – automated network planning and

components’ startup (“plug-and-play” solutions).

Self-Optimization – in terms of e.g. coverage, capacity,

load, etc. by means of network parameterization

tuning

Self-Healing – detection and diagnosis of network

breakdowns in automatic manner with consecutive

recovery actions.

7

book by S. Hämäläinen et. al. LTE Self-Organizing Networks (SON):

Network Management Automation for Operational Efficiency


Self-Healing

Self-Healing includes:

1. Fault detection

2. Fault diagnosis (root cause analysis)

3. Recovery planning

4. Recovery execution

In PhD the idea is to use more intelligent data

mining algorithms in self-healing.

8


Minimization of Drive Tests (MDT)

Drive testing – Method of measuring and assessing the

coverage, capacity and QoS of a mobile radio network

using special equipment

MDT – is part of coverage&capacity optimization in SON

UE measurements and control plane reporting +

existing network data

Location information

should be available

9 [Agilent E6474A Drive Test Network Optimization Platform]


PROBLEM DESCRIPTION

Research methodology, problem statement

10


Sleeping Cell Problem

Sleeping Cell (SC) - is a situation when Base Station

(BS) failure is not recognized by the operator as

there is no alarm triggered.

Sleeping Cell term includes:

– Harware failures – cable, antenna, amplifier problems

– Software failures – control channels failures, etc

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Research Methodology

Modeling

• Simulator features

• SC failure

Simulations

• Performance data is collected with MDT function

Data Mining on the basis of MDT data

12

Research is a design science type of study.

Data for the analysis is generated using simulator(-s)

of LTE mobile network (e.g. NS-3)


Demonstration

Show video

13


Data Mining Part

Data mining algorithms for normalization,

classification, clustering and dimensionality reduction

used so far include:

– K-means clustering

– K-nearest neigbors classification algorithm

– DBSCAN (Density-based Spatial Clustering of Applications

with Noise)

– CBLOF (Cluster Based Local Outliers Factor)

– PCA - Principal Component Analysis

– Diffusion maps

– N-gram analysis

14


N-GRAM ANALYSIS FOR

SLEEPING CELL DETECTION

IN LTE NETWORKS

Detection of sleeping cell caused by random access failure

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Random Access Channel Sleeping Cell

RACH problem: Full coverage, but NO handovers

Collect MDT data log

Analyze sequences of MDT events with N-gram

Compare Sleeping Cell detection approaches

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Simulations

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MDT Events

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N-gram is the way for analysing the sequences of events

This approach implies counting how many times each

combination (sequence) of events of length N has appeared in

the dataset.

Example:

– We have an alphabet of 3 events: [a, b, c]

– Vector A = [a b c a c a a c a b a c ]

– N = 2.

– The result of 2 gram analysis of vector A would be a matrix:

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N-gram Analysis

Sequence aa ab ac ba bb bc ca cb cc

Frequency 1 1 3 0 0 1 3 0 0


2-gram Matrices after Preprocessing

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10^2 = 100 combinations

We remove all rare 2-grams and in the end we have

32 combinations


Clustering

PCA reduces dimensionality from 32 to 3

FindCBLOF is applied for low dimensional data

– FindCBLOF clusters 113 testing samples as abnormal user and 205 as

normal user

21

Testing Training


Sleeping Cell Detection

Sleeping Cell detection is

based on number of

abnormal user visits in each

base stations’ dominance

area

(Abnormal) users visit in

many cells during the call

– Average 6 visits

– Range from 3 to 11

Cell 28 neighbors:

– 24, 27, 29, 39, 41, 44

22


Symmetry Analysis (1)

The assumption is that we know the behavior of 2-

grams based on training data. – When user movements are random, it is expected that any 2-gram

should be somewhat balanced

Analyzing abnormal users’ most common 2-grams it is

possible to detect some unbalanced 2-grams which

can be used as an indication of problem in particular

cell or area

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Symmetry Analysis (2)

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CONCLUSIONS

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Current Work Status

Started in March 2011

55 ECTS credits

3 conference papers + 1 under review

2nd author in journal paper

Estimated completion time is March 2015

26


Publications

Fedor Chernogorov, Jussi Turkka, Tapani Ristaniemi, Amir Averbuch:

Detection of Sleeping Cells in LTE Networks Using Diffusion Maps. VTC

Spring 2011: 1-5

Fedor Chernogorov, Timo Nihtilä: QoS Verification for Minimization of

Drive Tests in LTE Networks. VTC Spring 2012: 1-5

Jussi Turkka, Fedor Chernogorov, Kimmo Brigatti, Tapani Ristaniemi,

Jukka Lempiäinen: An Approach for Network Outage Detection from

Drive-Testing Databases. Journal Comp. Netw. and Communic. 2012

(2012)

Fedor Chernogorov, Tapani Ristaniemi, Kimmo Brigatti, Sergey Chernov:

N-gram Analysis For Sleeping Cell Detection in LTE Networks, ICASSP

2013

1 conference paper is under review

27


Thank you!

Fedor Chernogorov

[email protected]

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