Classification of Cells Based on Mobile Network Context Information

Transcription

1 Classification of Cells Based on Mobile Network Context Information Simon Lohmüller University of Augsburg, Nokia Sören Hahn, Thomas Kürner Technical University of Braunschweig Dario Götz, Andreas Eisenblätter atesio GmbH Lars Christoph Schmelz Nokia 1 Nokia Solutions and Networks 2015

2 Motivation SON Function Configuration Measurements / KPIs SCP SCP SCP SCP SON Function Network Configuration Parameters SON Function behaviour (impact on KPI values) can be influenced through SON Function Configuration Parameters (SCPs) by adjusting SCP Values (SCVs) 2 Nokia Solutions and Networks 2015

3 Motivation SON Management I want the network to. I want the network to. Manual SON Function Configuration Overcome Manual Gap Technical Objectives 3 Nokia Solutions and Networks 2015

4 Basics SON Objective Manager Operator Domain Manufacturer Domain Manufacturer A Manufacturer B Objective Model Context Model SON Function Model A SON Function Model B SON Objective Manager SCV Set A SCV Set B SON Function A SON Function B 4 Nokia Solutions and Networks 2015

5 Problem Description KPI Target Definition in the Mobile Network Different KPI targets for different areas in the network KPI targets may change over time KPI targets A KPI targets C KPI targets B 5 Nokia Solutions and Networks 2015

6 Problem Description Goal for SON Objective Manager Goal Find suitable SCV Sets for the SON Functions implemented at each cell for every condition the cell may be in SON Objective Manager Mapping Conditions X Cells + SCV Sets Problem: Impossible to select suitable SCV Sets for each individual cell manually 6 Nokia Solutions and Networks 2015

7 Cell Type Problem Description Context Context Space Context Abstract description of a cell s properties and capabilities as well as the environment and situation it operates in Context Space All possible context combinations that may exist One dimension for each context parameter Available Technology Cell Type {Pico, Micro, Macro} Cell Technology {LTE-1800, LTE-2600, UMTS-2100, GSM-900} Micro Macro Pico Problem: n-dimensional context space with possibly infinite context attributes 7 Nokia Solutions and Networks 2015

8 Concept Introduction of Context Attributes First Reduction Introduction of context attributes SCV Set selection based on description of cell s context SON Objective Manager Mapping Conditions X Cells Contexts + SCV Sets Assumption: Cells in the same context (i.e., operating in the same situation and environment) can be handled in a similar way 8 Nokia Solutions and Networks 2015

9 Concept Introduction of Objectives Objectives Depend on the cell s context Formulated by the network operator SON Objective Manager Mapping Conditions X Cells Contexts Objectives + SCV Sets Problem: Impossible to define objectives for each individual cell context manually Assumption: Cells in equal context have equal objectives 9 Nokia Solutions and Networks 2015

10 Cell Type Cell Type Concept Context Space Context Classes Context Space all possible context combinations that may exist one dimension for each context parameter Context Classes Combination of context attributes Each cell class represents certain cells in the network Available Technology Available Technology Micro Macro Pico Micro Macro A C B Pico Problem: n-dimensional context space with possibly infinite context attributes Solution: Partitioning of context into context classes 10 Nokia Solutions and Networks 2015

11 Concept Reduction to Cell Classes Classes Reduce the amount of objectives one objective per cell class Reduce the complexity of the context space partitioning into cell classes SON Objective Manager Mapping Conditions X Cells Contexts Classes Objectives + SCV Sets 11 Nokia Solutions and Networks 2015

12 Concept SON Function Model Mapping SON Function Model (SFM) Predicts the expected network behaviour in terms of KPIs for a specific SCV Set SON Function Model Mapping Contexts X SCV Sets Classes X SCV Sets Behaviours Assumption: Behaviour depends on cell context and the environment context dependent effects in the SFM 12 Nokia Solutions and Networks 2015

13 Concept Combined Transformation Process SON Objective Manager Combines both mapping processes in order to reduce complexity Determines the appropriate objective for a cell under a given condition based on cell class definition Behaviour prediction in the SFM enables selection of SCV Sets that are in line with the given objectives SON Objective Manager Mapping Conditions X Cells Contexts Classes Objectives + SCV Sets SON Function Model Mapping Classes X SCV Sets Behaviours 13 Nokia Solutions and Networks 2015

14 Implementation Context Attribute Identification Techniques Expert Knowledge Basic set of context attributes can be provided manually by the operator Problems Hard to classify thousands of cells in the network Cell s context may change over time Automation Determine context attributes of a cell with regards to the type of land it covers E.g., urban vs. rural, high-speed mobility vs. normal mobility Use so-called land use maps (or clutter maps ) and pixel maps Example Large parts of cell s footprint consists of the land use classes low-density area and forest Cell will be classified as rural 14 Nokia Solutions and Networks 2015

15 Implementation Detection of Faults in the Assignment Problem Introducing an automated mechanism raises questions about How can results be verified? How may faults be detected? Solution Fault detection by analysing the similarity of the behaviour of cells belonging to the same context class Statistical outlier detection Classification methods 15 Nokia Solutions and Networks 2015

16 Conclusion and Future Work Conclusion A mechanism to classify cells based on network context information has been introduced complexity in the management of the network can be significantly reduced Applications for Context and Classes in the management of a SON have been introduced Methods to classify cells and detect incorrectly classified cells have been explained Future Work Apply self-learning techniques (e.g., to deal with wrong cell class assignment) Ultimate goal: Facilitate the adjustment of cells and the SON Function running on that cell individually so that they best fulfil given operator objectives 16 Nokia Solutions and Networks 2015

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