enhanced performance monitoring and self-organization...
TRANSCRIPT
UNIVERSITY OF JYVÄSKYLÄ
Enhanced Performance
Monitoring and Self-Organization
for Future Mobile Networks
Fedor Chernogorov
UNIVERSITY OF JYVÄSKYLÄ
Introduction
Supervisor: Dr. Prof. Tapani Ristaniemi
Thesis format: collection of articles
Current state:
– 2 conference papers
– 2nd author in journal article (under review)
It has been 1 year since the beginning
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Research Objectives
Optimization and increase in reliability of
modern cellular networks:
– Development of self-optimization and self-
healing algorithms for novel mobile
networks
– Studies on Minimization of Drive Tests
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Operational Management in Cellular
Mobile Networks
Nowadays mobile networks are planned and managed
manually!
Averaged Key Performance Indicators (KPIs)
Optimization is started mainly in cases of major
breakdowns or customers’ complaints (and takes
weeks or even months…or seconds).
Future networks are even more complex – multi-
standard (2G, 3G, 3.5G), still multi-vendor, multi-
parameter
There is a lot of room for optimization!
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Self-Organization in Future Cellular
Networks
Self-Organizing Networks (SON) – utilization of
automatic algorithms for improvement of wireless
networks’ operation in terms of configuration,
performance, fault detection and security.
– 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 manner5
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Basis for SON solutions
Availability of higher number of KPIs of the network –
extended reporting, by the User Equipments (UEs).
Cognitive/Self-x algorithms utilize data mining and
machine learning techniques:
– Normalization, classification, clustering, dimensionality
reducition methods.
Rule-based approach (e.g. profile creation)
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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 is available
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[Agilent E6474A Drive Test Network Optimization Platform]
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Examples of MDT data
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Sleeping Cell Problem
“Sleeping Cell” is a situation when Base Station (BS)
failure is not recognized by the operator as there is no
alarm triggered.
In other words, BS doesn’t provide service to the
users, but seems to be non-faulty for the operator
(also know as “latent fault case”)
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Reasons for Sleeping Cell Appearance
Sleeping cell can be caused by hardware failure or
misconfiguration, e.g.:
– If a cell continues transmitting but does not accept random
access preambles, it will simply generate interference.
In many cases the reason is not known / hard to find.
Simple analogy:
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Problem: Cell 8 is “sleeping”,
because of HW failure.
1. Network measurements
gathered by means of MDT
form multidimensional data
space.
2. Irrelevant, erroneous data
is filtered out
Sleeping Cell Detection (1)
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Sleeping Cell Detection (2)
3. Dimensionality of this dataset
is reduced with nonlinear
algorithm - Diffusion Maps
4. In low dimension data is
clustered. Smaller cluster is
marked as abnormal
5. Problematic samples are
located in the real network
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OUTAGE
