Post on 06-Apr-2018
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PhD Thesis Seminar
Presentation 2
Fedor Chernogorov
17.04.2013
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Structure of Presentation
Introduction and Background
Research problem description
Example of research work – conference paper
presentation
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Introduction
Scientific advisor: Dr. Prof. Tapani Ristaniemi
Thesis format: collection of articles
Working title: Enhanced Performance Monitoring and
Self-Organization for Future Mobile Networks
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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
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* 3GPP – 3rd Generation Partnership Project
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BACKGROUND
Self-Organizing Networks, Minimization of Drive Tests
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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
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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.
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book by S. Hämäläinen et. al. LTE Self-Organizing Networks (SON):
Network Management Automation for Operational Efficiency
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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.
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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]
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PROBLEM DESCRIPTION
Research methodology, problem statement
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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
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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)
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Demonstration
Show video
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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
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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
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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
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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
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Testing Training
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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
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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
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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
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Thank you!
Fedor Chernogorov
fedor.chernogorov@jyu.fi