Capacity Optimization for Self-organizing Networks: Analysis and Algorithms

1. Capacity Optimization for Self-organizing Networks: Analysis and Algorithms Philipp Hasselbach Philipp Hasselbach

2. Motivation • Inhomogeneous capacity demand • Rush hour traffic • Concerts, sports tournaments • Change in user behaviour • Capacity Optimization • As much capacity asrequired • At all times and all places • Achieved by allocation of cell bandwidth and transmit power to the cells Philipp Hasselbach

3. Downlink considered Link capacity influencing factors User position Attenuation Shadowing Inter-cell interference Cell capacity influencing factors User distribution Service type Scheduling Transmit power Cell bandwidth Inter-cell inter-ference power SINR of user k Noise power Capacity in Cellular Networks Philipp Hasselbach

4. 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 Source:FP7 SOCRATES Self-organizing Networks (SONs) SONS: Shift of paradigm Philipp Hasselbach

5. Real-time capabilities Treatment of large networks Accurate results Reliable operation Depends on user distribution environment Inter-cell interference (ICI) Interdependencies among cells and users Source:FP7 SOCRATES Automatic Capacity Optimization for SONs SON requirements Capacity optimization High complexity, excessive signaling 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. 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

8. 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

9. Cell-centric Network Model User bit rate PBR-Characteristic Cell throughput • SINR measurements • User distribution, environment model Philipp Hasselbach

10. Cell-centric Network Model User bit rate PBR-Characteristic Cell throughput • User bit rate pdf • empiric • theoretic • SINR measurements • User distribution, environment model • Number of users • User QoSrequirements Philipp Hasselbach

11. Cell-centric Network Model User bit rate PBR-Characteristic Cell throughput • Cell throughput cdf • empiric • theoretic • User bit rate pdf • empiric • theoretic • SINR measurements • User distribution, environment model • Number of users • User QoSrequirements • Outage probability p • Cell bandwidth B • Transmit power P Cell throughput in Mbit/s p Philipp Hasselbach

12. PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i PBN-Characteristic Relates transmit power, cell bandwidth, number of users of cell i PBR- and PBN-Characteristic Cell throughput in Mbit/s Number of users Philipp Hasselbach

13. PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i PBN-Characteristic Relates transmit power, cell bandwidth, number of users of cell i PBR- and PBN-Characteristic Cell throughput in Mbit/s Power ratio: relates transmit powerto average inter-cell interference power Number of users Philipp Hasselbach

14. PBR-Characteristic Relates transmit power, cell bandwidth, cell throughput of cell i PBN-Characteristic Relates transmit power, cell bandwidth, number of users of cell i PBR- and PBN-Characteristic Cell throughput in Mbit/s Available for different schedulers Number of users Available for different schedulers Philipp Hasselbach

15. 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

16. Self-organizing control loop: Network state optimization 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 Networkstateevaluation Collection of measure-ments Networkcapacityoptimisation Cell throughput in Mbit/s Cellularradionetwork Self-organizing Approach Philipp Hasselbach

17. Current network state Number of users in cell i: Cell bandwidth: Power ratio: Number of users that can be supported by the cell (obtained from PBN-Characteristic): : no action : network optimization Number of users Network State Evaluation Philipp Hasselbach

18. Network Capacity Optimization Optimization problems Optimization approaches Philipp Hasselbach

19. Network Capacity Optimization Optimization problems Optimization approaches Central and distributed solving algorithms for analysis and implementation Philipp Hasselbach

20. 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

21. Inhomogeneous capacity demand: hotspot scenarios users in hotspot cell, users in non-hotspot cell Hotspot factor Wrap-around technique to avoid border effects 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 Simulation Approach for Functional Analysis Single hotspot scenario Multi hotspot scenario Cluster hotspot scenario Philipp Hasselbach

22. Simulation Parameters for Functional Analysis Philipp Hasselbach

23. Network Throughput Optimization, Single Hotspot Scenario PF scheduling FT scheduling Philipp Hasselbach

24. Network Throughput Optimization, Coordinated Bandwidth Allocations Cluster HS Scenario Multi HS Scenario Philipp Hasselbach

25. 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

26. 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

27. Real-World-Analysis: Hotspot Strength and Strongest Hotspots Maximum hotspot strength Strongest hotspot Philipp Hasselbach

28. Real-World-Analysis: Hotspot Strength and Strongest Hotspots Maximum hotspot strength Strongest hotspot Philipp Hasselbach

29. Real-World-Analysis: Hotspot Strength and Strongest Hotspots Network throughput,FT scheduling Strongest hotspot Philipp Hasselbach

30. 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

31. 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

32. Backup Philipp Hasselbach

33. Power-Bandwidth Characteristics User distribution PDF of the bandwidth required by user k K independent users Central Limit Theorem Bandwidth required by user k PDF of the bandwidth required by the cell Bandwidth required by the whole cell Philipp Hasselbach

34. CDF of the bandwidth required by the cell Probability that sufficient bandwidth is allocated Cell outage probability Probability that allocated bandwidth is not sufficient Cell Outage Probability Bandwidth required by the cell Philipp Hasselbach

35. Philipp Hasselbach

36. Motivation • Fluctuating capacity demand • Rush hour traffic • Concerts, sports tournaments • Change in user behaviour • Change in environment • Capacity Optimization • As much capacity asrequired • At all times and all places Philipp Hasselbach

37. Real-time capabilities Accurate results Reliable operation Complex modeling Large number of users and BSs Effects of the user distribution Effects of the environment Interdependencies among cells and users Source:FP7 SOCRATES Automatic Capacity Optimization for SONs SONs Capacity optimization Philipp Hasselbach

38. Real-time capabilities Accurate results Reliable operation Complex modeling Effects of the user distribution Effects of the environment Inter-cell interference (ICI) Interdependencies among cells and users Source:FP7 SOCRATES Automatic Capacity Optimization for SONs SONs Capacity optimization Philipp Hasselbach

39. Philipp Hasselbach

40. Cell-centric Network Model • User distribution, environment model • SINR measurements User QoS requirements • Outage probability • Cell bandwidth • Transmit power Cell throughput in Mbit/s Philipp Hasselbach

41. Cell-centric Network Model • User bit rate pdf • empiric • theoretic • Number of users • User QoSrequirements • User distribution, environment model • SINR measurements • Outage probability • Cell bandwidth B • Transmit power P Cell Performance for (B,P) Philipp Hasselbach

42. Reduced complexity due to focus on cells User QoS requirements considered Relation between cell bandwidth, transmit power and cell performance PBR-Characteristic Cell Performance for (B,P) • For different • Cell bandwidth B • Transmit power P Cell throughput in Mbit/s Philipp Hasselbach

43. Philipp Hasselbach

44. Model the interdependence oftransmit power and cellbandwidth Contain information on userdistribution, environment,inter-cell interference Analytic derivation available Measurement based derivation available, determined fromstandard system measurements(attenuation, SINR) User distribution Environment model SINR measurements Random Variable transformation Measurement data transformation Modeling equations Cell-centric Network Model Theoretic Approach Practical Approach Philipp Hasselbach

45. Cell-centric Network Model • User distribution, environment model • SINR measurements • Outage definition • Cell bandwidth • Transmit power Number ofusers Philipp Hasselbach

46. Philipp Hasselbach

47. Automatic Capacity Optimization Approaches Uncoordinated/scheduling based (State of the art): Coordinated (new): Can I take SC 1? I take SC 1. OK, I take SC 2 I take SC 1. SC1 SC2 SC1 SC1 Inter-BScommunication LocalScheduling LocalScheduling + : Collisions can be avoided QoS- : Complexity? Implementation? + : easy implementation- : Collisions, QoS? Philipp Hasselbach

48. Two Alternative SO Approaches Uncoordinated: Coordinated: Can I take SC 1? I take SC 1. OK, I take SC 2 I take SC 1. SC1 SC1 SC1 SC1 Inter-BScommunication LocalScheduling LocalScheduling Power-Bandwidth Characteristicfor approach realization and per-formance analysis Power-Bandwidth Characteristicfor performance analysis Philipp Hasselbach

49. Networkparameteroptimisation Resource allocation to cells Networkstateevaluation Networkparameteradjustment Sched.cell 1 Sched.cell 2 Sched.cell N Source: 3GPP General System Concept Self-organising functionality/ Self-organising control loop Hierarchical approach Resource allocation to users,no inter-cell scheduling Philipp Hasselbach