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Capacity Optimization for Selforganizing Networks: Analysis and Algorithms Philipp Hasselbach Philipp Hasselbach Motivation Inhomogeneous capacity demand Rush hour traffic Concerts, sports tournaments Change in user behaviour t t1 Capacity Optimization t t2 Philipp Hasselbach As much capacity as required At all times and all places Achieved by allocation of cell bandwidth and transmit power to the cells 2 Capacity in Cellular Networks Ptx , B PI Downlink considered 1 , PN Link capacity influencing factors User position Attenuation Shadowing Inter-cell interference Cell capacity influencing factors User distribution Service type Scheduling Philipp Hasselbach PI 2 , PN Ptx B PI k PN Transmit power Cell bandwidth Inter-cell interference power SINR of user k Noise power 3 Self-organizing Networks (SONs) Drivers High complexity of mobile radio technology Operation of several networks of different technologies Need to reduce CAPEX and OPEX Autonomous operation In configuration, optimization, healing Circumventing classical planning and optimization processes SONS: Shift of paradigm Philipp Hasselbach Source: FP7 SOCRATES 4 Automatic Capacity Optimization for SONs SON requirements Source: FP7 SOCRATES Real-time capabilities Treatment of large networks Accurate results Reliable operation Capacity optimization Depends on user distribution environment Inter-cell interference (ICI) Interdependencies among cells and users High complexity, excessive signaling Philipp Hasselbach 5 Outline Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization Simulation and Analysis Functional Analysis Real-World Analysis Summary Philipp Hasselbach 6 Outline Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization Simulation and Analysis Functional Analysis Real-World Analysis Summary Philipp Hasselbach 7 Cell-centric Network Model: Requirements Application for allocation of resources cell bandwidth and transmit powers to the cells Modeling of the relation between cell bandwidth, transmit power and cell performance Low complexity Consideration of User QoS requirements User distribution Environment Inter-cell interference Interdependencies among cells Philipp Hasselbach 8 Cell-centric Network Model User bit rate Cell throughput PBR-Characteristic •SINR measurements •User distribution, environment model Philipp Hasselbach 9 Cell-centric Network Model User bit rate Cell throughput PBR-Characteristic •User bit rate pdf • empiric • theoretic •SINR measurements •User distribution, environment model Philipp Hasselbach •Number of users •User QoS requirements 10 User bit rate •SINR measurements •User distribution, environment model p Philipp Hasselbach Cell throughput PBR-Characteristic •User bit rate pdf • empiric • theoretic •Cell throughput cdf • empiric • theoretic •Number of users •User QoS requirements •Outage probability p •Cell bandwidth B •Transmit power P Cell throughput in Mbit/s Cell-centric Network Model 11 Number of users Cell throughput in Mbit/s PBR- and PBN-Characteristic Philipp Hasselbach PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i Ri f R Bi , i PBN-Characteristic Relates transmit power, cell bandwidth, number of users of cell i Ni f N Bi , i 12 Cell throughput in Mbit/s PBR- and PBN-Characteristic PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i Ri f R Bi , i Number of users Power ratio: relates transmit power to average inter-cell interference power PBN-Characteristic Philipp Hasselbach Relates transmit power, cell bandwidth, number of users of cell i Ni f N Bi , i 13 Number of users Cell throughput in Mbit/s PBR- and PBN-Characteristic PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i Ri f R Bi , i Available for different schedulers PBN-Characteristic Relates transmit power, cell bandwidth, number of users of cell i Ni f N Bi , i Available for different schedulers Philipp Hasselbach 14 Outline Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization Simulation and Analysis Functional Analysis Real-World Analysis Summary Philipp Hasselbach 15 Self-organizing Approach Self-organizing control loop: Network state optimization Collection of measurements Network capacity optimisation Cellular radio network Philipp Hasselbach Cell throughput in Mbit/s Network state evaluation Application of PBR-/PBN-Characteristic Determination of possible performance Comparison with required performance Decision to take action Network capacity optimization Definition of optimization problems Application of PBR-/PBN-Characteristic in objective function and constraints Solution of optimization problems to obtain resource allocation to cells Constant cell sizes 16 Network State Evaluation ~ Number of users in cell i: N i Cell bandwidth: Bi Power ratio: i Number of users Current network state Bi Number of users that can be supported by the cell (obtained from PBN-Characteristic): N i ~ N i N i N : no action ~ N i N i N : network optimization Philipp Hasselbach i Ni f N Bi , i 17 Network Capacity Optimization Optimization problems max Capacity P, B s.t. min. user QoS Optimization approaches Network throughput R P ,B max. transmit power max. cell bandwidth feasibilit y Total number of users N Transmit power P Cell bandwidth B Joint P,B R Ri Bi , i i N N i Bi , i i Philipp Hasselbach 18 Network Capacity Optimization Optimization problems max Capacity P, B s.t. min. user QoS Optimization approaches Network throughput R P ,B max. transmit power max. cell bandwidth feasibilit y R Ri Bi , i i N N i Bi , i Total number of users N Transmit power P Cell bandwidth B Joint P,B Central and distributed solving algorithms for analysis and implementation i Philipp Hasselbach 19 Outline Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization Simulation and Analysis Functional Analysis Real-World Analysis Summary Philipp Hasselbach 20 Simulation Approach for Functional Analysis Single hotspot scenario Cluster hotspot scenario Inhomogeneous capacity demand: hotspot scenarios N hs users in hotspot cell, N 0 users in non-hotspot cell Hotspot factor hs N hs N0 Wrap-around technique to avoid border effects Philipp Hasselbach Multi hotspot scenario Evaluation of capacity optimization approaches w.r.t. hotspot distribution Evaluation for different hotspot strengths w/o coordination of bandwidth allocations of neighbored cells Mitigation of inter-cell interference LTE-typical simulation parameters 21 Simulation Parameters for Functional Analysis Cell radius R 250 m Number of cells 39 User distribution uniform Propagation model 3GPP SCM Urban Macro Shadow fading variance 8 dB Max. transmit power 33 dBm Total system bandwidth 10 MHz Scheduling PF, FT Data rate per user 100 kbit/s Philipp Hasselbach 22 Network Throughput Optimization, Single Hotspot Scenario PF scheduling Philipp Hasselbach FT scheduling 23 Network Throughput Optimization, Coordinated Bandwidth Allocations Cluster HS Scenario Philipp Hasselbach Multi HS Scenario 24 Functional Analysis: Summary Adaptation of the network to inhomogeneous capacity demands achieved For strong inhomogeneous capacity demand coordination of bandwidth allocations required For FT scheduling coordination of bandwidth allocations required Transmit power allocation favorable with clustered hotspot cells Cell bandwidth allocation and joint allocation favorable with distributed hotspot cells Philipp Hasselbach 25 Simulation Approach for Real-World Analysis Scenario based on real network Network footprint from existing network Downtown area, 50 km², 46 sites, 126 sectors Pilot power receive strength prediction for each sector Determination of cell borders Inhomogeneous capacity demand According to user distribution estimation Based on DL throughput measurements 229 snapshots over 5 days Performance analysis Consideration of snapshots 10-50 Evaluation of performance in strongest hotspots Philipp Hasselbach 26 Real-World-Analysis: Hotspot Strength and Strongest Hotspots Maximum hotspot strength Philipp Hasselbach Strongest hotspot 27 Real-World-Analysis: Hotspot Strength and Strongest Hotspots Maximum hotspot strength Philipp Hasselbach Strongest hotspot 28 Real-World-Analysis: Hotspot Strength and Strongest Hotspots Network throughput, FT scheduling Philipp Hasselbach Strongest hotspot 29 Outline Cell-centric Network Model Requirements and Derivation PBR- and PBN-Characteristic Automatic Capacity Optimization for SONs Self-Organizing Approach Network State evaluation Network Capacity Optimization Simulation and Analysis Functional Analysis Real-World Analysis Summary Philipp Hasselbach 30 Summary Cell-centric network modeling proposed PBR- and PBN-Characteristic Provides accurate modeling for automatic capacity optimization for SONs Avoids high complexity and high signaling effort Self-Organizing Approach proposed Application of cell-centric network model Central and distributed implementations for analysis and practical implementation Simulative verification In artificial scenarios and real-world scenario Adaptation of the network to inhomogeneous capacity demands shown Philipp Hasselbach 31 Backup Philipp Hasselbach 32 Power-Bandwidth Characteristics f r , r , User distribution f r , r , f K independent users ~ Bk Rbit,k log 2 1 k K ~ ~ Bcell Bk k 1 Philipp Hasselbach Bandwidth required by user k Bandwidth required by the whole cell PDF of the bandwidth required by user k ~ f B~k Bk , Ptx , PI Central Limit Theorem PDF of the bandwidth required by the cell ~ 2 f B~cell ( Bcell , Ptx , PI ) ~ Ν ( cell , cell ) F ( Bcell , Ptx , PI ) 33 Cell Outage Probability CDF of the bandwidth required by the 1 pcell cell Probability that sufficient bandwidth is allocated ~ Prob Bcell Bcell F ( Bcell , Ptx , PI ) Cell outage probability Probability that allocated bandwidth is not sufficient pcell 1 F ( Bcell , Ptx , PI ) Philipp Hasselbach ~ Bandwidth Bcell required by the cell Bcell 34 Philipp Hasselbach 35 Motivation Fluctuating capacity demand Rush hour traffic Concerts, sports tournaments Change in user behaviour Change in environment t t1 Capacity Optimization As much capacity as required At all times and all places t t2 Philipp Hasselbach 36 Automatic Capacity Optimization for SONs SONs Source: FP7 SOCRATES Real-time capabilities Accurate results Reliable operation Philipp Hasselbach Capacity optimization Complex modeling Large number of users and BSs Effects of the user distribution Effects of the environment Interdependencies among cells and users 37 Automatic Capacity Optimization for SONs SONs Source: FP7 SOCRATES Real-time capabilities Accurate results Reliable operation Philipp Hasselbach Capacity optimization Complex modeling Effects of the user distribution Effects of the environment Inter-cell interference (ICI) Interdependencies among cells and users 38 Philipp Hasselbach 39 Cell-centric Network Model •User distribution, environment model •SINR measurements Cell throughput in Mbit/s User QoS requirements Philipp Hasselbach •Outage probability •Cell bandwidth •Transmit power 40 Cell-centric Network Model •User bit rate pdf • empiric • theoretic •Number of users •User QoS requirements •User distribution, environment model •SINR measurements •Outage probability •Cell bandwidth B •Transmit power P Cell Performance for (B,P) Philipp Hasselbach 41 PBR-Characteristic Cell Performance for (B,P) Cell throughput in Mbit/s •For different • Cell bandwidth B • Transmit power P Philipp Hasselbach Reduced complexity due to focus on cells User QoS requirements considered Relation between cell bandwidth, transmit power and cell performance 42 Philipp Hasselbach 43 Cell-centric Network Model Theoretic Approach Model the interdependence of transmit power and cell bandwidth Contain information on user distribution, environment, inter-cell interference Analytic derivation available Measurement based derivation available, determined from standard system measurements (attenuation, SINR) Random Variable transformation Practical Approach Measurement data transformation Modeling equations Philipp Hasselbach 44 Cell-centric Network Model •User distribution, environment model •SINR measurements •Outage definition •Cell bandwidth •Transmit power Number of users Philipp Hasselbach 45 Philipp Hasselbach 46 Automatic Capacity Optimization Approaches Uncoordinated/scheduling based (State of the art): Can I take SC 1? I take SC 1. SC1 Local Scheduling I take SC 1. SC1 SC2 OK, I take SC 2 SC1 Local Scheduling + : easy implementation - : Collisions, QoS? Philipp Hasselbach Coordinated (new): Inter-BS communication + : Collisions can be avoided QoS - : Complexity? Implementation? 47 Two Alternative SO Approaches Uncoordinated: Coordinated: Can I take SC 1? I take SC 1. SC1 Local Scheduling I take SC 1. SC1 SC1 Local Scheduling Power-Bandwidth Characteristic for performance analysis Philipp Hasselbach SC1 OK, I take SC 2 Inter-BS communication Power-Bandwidth Characteristic for approach realization and performance analysis 48 General System Concept Hierarchical approach Self-organising functionality/ Self-organising control loop Network parameter optimisation Resource allocation to cells Sched. Sched. cell 1 cell 2 Sched. cell N Resource allocation to users, no inter-cell scheduling Philipp Hasselbach Network state evaluation Network parameter adjustment Source: 3GPP 49