This document discusses automated network design using intelligence and modeling. It summarizes the evolution of challenges from 2012 to 2018, including factors like network densification, interference, and new 5G technologies. Key aspects of the automated design process are modeling the environment, learning propagation models from measurements, understanding user demand patterns, and using this information to design and connect network sites in an optimal configuration. The goal is to maximize ROI and quality of service from the perspective of both the provider and users.

1. Automated HetNet
Design
CW TEC 2018
Iris Barcia
COO

2. We use intelligence to design the optimal network
• Provider Perspective
• Maximize ROI (optimal location)
• Service / User Perspective
• Context
• Requirements and expectations
5G - Public Safety Design Portland

3. ROI: Multiple Independent Variables
Technology Framework
Locations
Regulation
Time
G
Efficiency
Access
Power
Backhaul
Spectrum
Strategy

4. HetNet AutoDesign Workflow – Complexity

5. 2012: Challenges – Coverage Planning to Capacity Design
• Link Adaption
• Non-uniform Demand
• Network Densification
• Interference Continuum

6. 2018: Design Challenges – Capacity Planning
• Link Adaption
• Non-uniform Demand
• Network Densification
• Interference Continuum
• mMIMO
• 5G Numerology
• Slicing, Edge Computing
• New frequencies
• Densification (more)

7. Training and
modelling

8. Digitization of the Environment – Classification
Multiple data sources – geographical differences
Quantization Error < R/100
2012 – Environment Model, Lower Manhattan (Vector Clutter) 2018 – Environment Model, Lower Manhattan (DHM Clutter)

9. Manhattan

10. Manhattan

11. Manhattan

12. Manhattan

13. Boston

14. Boston

15. Boston

16. Boston

17. Dallas

18. Dallas

19. Dallas

20. Dallas

21. Dallas

22. Dallas

23. Learning
propagation
models
• The model understands the
environment and Autotunes
its properties

24. Network
Measurements

25. Network
Measurements

26. Classification

27. Geocorrection

28. Signal
Propagation

29. Network
Measurements

30. Classification

31. Signal
Propagation

32. AutoCorrect
Azimuth Error - Detection and Correction

33. Understanding user
behaviour - Demand
• Network Agnostic Crowdsourced
Information
• Continuous update
• Statistical Relevance
• Seamless Integration
• Context-aware sources
• Crime
• Rescue Centres
• Events
• Network Data Calibration
Composite Demand heatmap - user clustering and density

34. Social Networks
London

35. Social Networks
London

36. Social Networks
London

37. Social Networks
London

38. Social Networks
London

39. Manhattan

40. Manhattan

41. Manhattan

42. Manhattan

43. Dallas

44. Dallas

45. Dallas

46. Dallas

47. Design
AutoDesign
AutoConnect
AutoX-Workflows

48. Slide presented by Andy Sutton

49. 32553 sites
41949 radios
39 macro – Core Hubs
13 km x 13 km
1 Day
~ 90K households
Fixed Connection
Type
Connected
Housholds
Hub Edge 441
Tier 1 14133
Tier 2 8919
Tier 3 4481
Tier 4 2604
Tier 5 1803

50. AutoConnect – 5G Fixed Wireless
• 5G Integrated Access
and Backhauling
• Fully deterministic*
Increased latency
-120
-100
-80
-60
-40
-20
0
540
550
560
570
580
590
600
610
620
Edge Hub Tier 1 Tier 2 Tier 3 Tier 4 Tier 5
RSSI(dBm)
Bitrate(Mbps)
FAIRFAX COUNTY - 5G FIXED WIRELESS
Average of Uplink RSSI (dBm) Average of Downlink RSSI (dBm) Average of Uplink Bitrate (Mbps) Average of Downlink Bitrate (Mbps)

51. Edge Computing
Hubs

52. Inter-Hub
Connections

53. Tier 1

54. Tier 2

55. Tier 3

56. Tier 4

57. Tier 5

58. Core Hubs

59. Inter-Hub
Connections

60. Tier 1

61. Tier 2 + Wired

62. Tier 3 + Wired

63. Tier 4 + Wired

64. Tier 5 + Wired

70. Future
• Drones
• Context – aware, self-deploying solutions
• Ad-hoc structures
Madrid

71. Thank you
To design modern mobile networks we need precision
To get the best of the network we need a holistic analysis
To maximise ROI we need intelligence
Gelli, Wales
Keima
www.keima.com
info@keima.com