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Radio Challenges and Opportunities for
Large Scale Small Cell Deployments
Keima Wireless
Small Cell SIG Event
Cambridge, Oct 2012
Cell Planning 1947 to 1984
• “Mobile radiotelephones” first encounter interference
effects in 1946.
• The cellular concept1,2 invented c. 1946.
• Cell planning is managing interference while we:
– increase site numbers; and
– shrink the reuse distance.
2 D.H. Ring, Bell Labs – Telephony – Wide Area Coverage, 11 December 1947
1 J.R. Brinkley – J.I.E.E. – 1946, 93, Part III, pp.159-166
1947
“Cellular” Design in New York
8 km
Disruptive Events – Racal vs. BritishTelecom
• The start of the macro era:
• Cell Phones for the Masses…
• The new Vodafone network was launched on 1st January
1985. The smaller company’s innovative approach to
network planning allowed them to compete with the
UK’s biggest company.
• Vodafone became the largest mobile network …
1984
• Predicted coverage, Guildford 1984
• Main input: terrain
Cell Planning since 1984
25 25
1600
0
200
400
600
800
1000
1200
1400
1600
1800
Quantity of
Spectrum
Cell Spectrum
Efficiency
Number of
Cells
How do we provide capacity
to match Cooper’s demand
progression?
1947
“Cellular” Design in New York
8 km
2008
Macros
8 km
Disruptive Event - iPhone
The growth of data demand has doubled
Every 1 year
A rate greater than Cooper’s predictions.
Macros networks no longer effective.
Reduction in the cell size => small cells
The “Super Cooper” Expansion Challenges 1
1984 & 2012: cell location is still the key factor
64 QAM
64 QAM
64 QAM
Small Cell
Size : 100m
Macro Cell
Size : 500m
Wi-Fi AP
Size : 40m
QPSK
Quantisation Error
• Cell Radius, R.
• Quantisation1
ΔX < R/40
• So, if a cell is ~ 400 m, ΔX < 10 m.
1 Pete Bernardin and Kanagalu Manoj – IEEE Transactions on Vehicular Technology, Vol 49, No. 5, 2000
• We need to model the environment to 1 m accuracy.
Quantisation
Type R ΔX
Macro 500 m 12.5 m
Small Cell 100 m 2.5 m
Wi-Fi 40 m 1 m
Boston
Boston, raster clutter @ 25m
Boston, PV clutter @ 0.5 m
Boston
JFK Airport
JFK Airport
Demand estimates should take account of non uniformity. Techniques that
cannot identify usage clustering at the scale of the cell radius will have
limited use.
The “Super Cooper” Expansion Challenges 2
Non Uniform Demand
64 QAM
64 QAM
Clusters of
demand
Clustering Accuracy
• New small cells should be placed to coincide with areas of
highest demand (e.g. hotspots) to maximise capacity.
• Small cells have radii ~100 m which means their spectrally
efficient 64-QAM zones are ~50 m or less.
• Demand estimates should take account of non uniformity and
be capable of resolving clustering accuracy <50 m.
• Geotagged social data (Twitter, foursquare, …) use Wi-Fi
assisted GPS for location accuracy (including indoors) ~ 20 m or less
…
• (And using the Bernadine rule, we must use 1 m quantisation to
resolve such small demand clustering.)
7 km
Images generated in Overture by Keima
Twitter London
3 km
Twitter London
Images generated in Overture by Keima
1.5 km
Twitter London
Images generated in Overture by Keima
0.8 km
Twitter London
Images generated in Overture by Keima
400 m
High demand areas
Twitter London
Images generated in Overture by Keima
Demand is complex:
Road users;
Pedestrians;
Residential;
Business;
Railroads;
Etc.
200 m
Overall Demand (composite map)
Images generated in Overture by Keima
200 m
Indoor Demand
Images generated in Overture by Keima
200 m
Outdoor Demand
Images generated in Overture by Keima
What is the deployment focus?
Macro / outdoors, continuous coverage;
Macro / high mobility demand, highways;
Small cells / outdoors, hotspots;
Small cells + Wi-Fi / offload, POIs;
Etc
200 m
Outdoor Demand with Small Cells (light blue) and Wi-Fi + Small Cells (dark blue)
Images generated in Overture by Keima
Interference Continuum - Macro / Small Cell Power
Difference
Macros are 10 – 20 dB more powerful
Distant macros can have a significant effect on small cells
The “Super Cooper” Expansion Challenges 3
Interference
It is important to deal with such an interference continuum by
predicting the signal and interference across entire cities.
Interference continuum
Images generated in Overture by Keima
Boston
8 km
Boston
Images generated in Overture by Keima
4 km
Boston
Images generated in Overture by Keima
1 km
Boston
Images generated in Overture by Keima
Boston
Images generated in Overture by Keima
250 m
Boston
Images generated in Overture by Keima
100 m
Combinatorial Explosion – Complex interplay of dependent
objectives.
The “Super Cooper” Expansion Challenges 4
Cell planners will have to consider for each new cell during future
100,000+ rollouts:
Location, configuration and technology parameters;
Backhaul proximity and wireless clearance;
Rental costs;
Latency;
Etc.
2012
N2012 ~ 290,000
Images generated in Overture by Keima
Macros
500 km
2015
N2015 ~ 600,000
Images generated in Overture by Keima
Macros + Small Cells (estimate)
500 km
Cells will only be deployed in areas where there is a positive
return on investment.
Return on investment should consider:
Sites that pay their way by locating near high demand “hotspots”;
Sites with manageable interference impact; AND
Backhaul or rental costs are affordable.
Only by considering ALL objectives can we maximise return on
investment: plan small cells holistically.
• Automation seeks to maximise RoI by considering:
– Optimal traditional towers;
– Optimal utility poles;
– Optimal wall mounting;
– Suggesting search ring;
– Backhaul costs; rental costs;
– Etc.
Example 1
• New York Case Study:
– Low powered small cells (1W);
– Street furniture: lighting fixtures, kiosks, power lines, etc.;
– Primary and secondary attachments;
– Wireless and fiber backhaul;
– High spectral efficiency.
Total candidate set: > 470,000
Small cells (1W) required: 1852
Primary location – green
Secondary location – orange
Manhattan
Images generated in Overture by Keima
1 km
All candidate locations are street
furniture elements -> road alignment
0.5 km
Manhattan
Images generated in Overture by Keima
0.5 km
Manhattan
Images generated in Overture by Keima
Number of cells
follows demand
Holistic
Analyses
1 km
Manhattan
Images generated in Overture by Keima
Wireless @ 60 GHz
0.5 km
Manhattan
Images generated in Overture by Keima
Wireless @ 10 GHz
0.5 km
Manhattan
Images generated in Overture by Keima
Fibre Routes
250 m
Manhattan
Images generated in Overture by Keima
0.5 km
Manhattan
Images generated in Overture by Keima
CINR
1 Mbps cell edge performance
100 m
Manhattan
Images generated in Overture by Keima
Manhattan
Images generated in Overture by Keima
Location is key to maximise spectral efficiency
Data accuracy more important than ever (1m resolution)
Interference continuum
Different environments and different cell types
Non uniform demand
Design focus is to maximise spectrum utilisation
Demand clusters served by 64 QAM zone
Automation
Deployable business case depends on holistic network design
The “Super Cooper” Expansion Conclusions
Example 2
• London Case Study:
– Low powered small cells (2 W) + Wi-Fi systems;
– Street furniture: lighting fixtures, kiosks, power lines, etc.;
– Wireless and fibre backhaul;
– High spectral efficiency.
Indoors Demand Outdoor Demand with selected Small Cells and Wi-Fi
Small cell service area Wi-Fi service area
800 m
NLOS Connections
London
Images generated in Overture by Keima
800 m
10 GHz Fresnel Connections
London
Images generated in Overture by Keima
800 m
60 GHz Fresnel Connections
London
Images generated in Overture by Keima
London
Images generated in Overture by Keima
London
Images generated in Overture by Keima
Thank You
Contact Us
Iris Barcia
iris.barcia@keima.co.uk
Simon Chapman
simon.chapman@keima.co.uk

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Radio Challenges and Opportunities for Large Scale Small Cell Deployments

  • 1. Radio Challenges and Opportunities for Large Scale Small Cell Deployments Keima Wireless Small Cell SIG Event Cambridge, Oct 2012
  • 2. Cell Planning 1947 to 1984 • “Mobile radiotelephones” first encounter interference effects in 1946. • The cellular concept1,2 invented c. 1946. • Cell planning is managing interference while we: – increase site numbers; and – shrink the reuse distance. 2 D.H. Ring, Bell Labs – Telephony – Wide Area Coverage, 11 December 1947 1 J.R. Brinkley – J.I.E.E. – 1946, 93, Part III, pp.159-166
  • 4. Disruptive Events – Racal vs. BritishTelecom • The start of the macro era: • Cell Phones for the Masses… • The new Vodafone network was launched on 1st January 1985. The smaller company’s innovative approach to network planning allowed them to compete with the UK’s biggest company. • Vodafone became the largest mobile network …
  • 5. 1984 • Predicted coverage, Guildford 1984 • Main input: terrain
  • 6. Cell Planning since 1984 25 25 1600 0 200 400 600 800 1000 1200 1400 1600 1800 Quantity of Spectrum Cell Spectrum Efficiency Number of Cells How do we provide capacity to match Cooper’s demand progression?
  • 9. Disruptive Event - iPhone The growth of data demand has doubled Every 1 year A rate greater than Cooper’s predictions. Macros networks no longer effective. Reduction in the cell size => small cells
  • 10. The “Super Cooper” Expansion Challenges 1 1984 & 2012: cell location is still the key factor 64 QAM 64 QAM 64 QAM Small Cell Size : 100m Macro Cell Size : 500m Wi-Fi AP Size : 40m QPSK
  • 11. Quantisation Error • Cell Radius, R. • Quantisation1 ΔX < R/40 • So, if a cell is ~ 400 m, ΔX < 10 m. 1 Pete Bernardin and Kanagalu Manoj – IEEE Transactions on Vehicular Technology, Vol 49, No. 5, 2000
  • 12. • We need to model the environment to 1 m accuracy. Quantisation Type R ΔX Macro 500 m 12.5 m Small Cell 100 m 2.5 m Wi-Fi 40 m 1 m
  • 19. Demand estimates should take account of non uniformity. Techniques that cannot identify usage clustering at the scale of the cell radius will have limited use. The “Super Cooper” Expansion Challenges 2 Non Uniform Demand 64 QAM 64 QAM Clusters of demand
  • 20. Clustering Accuracy • New small cells should be placed to coincide with areas of highest demand (e.g. hotspots) to maximise capacity. • Small cells have radii ~100 m which means their spectrally efficient 64-QAM zones are ~50 m or less. • Demand estimates should take account of non uniformity and be capable of resolving clustering accuracy <50 m. • Geotagged social data (Twitter, foursquare, …) use Wi-Fi assisted GPS for location accuracy (including indoors) ~ 20 m or less … • (And using the Bernadine rule, we must use 1 m quantisation to resolve such small demand clustering.)
  • 21. 7 km Images generated in Overture by Keima Twitter London
  • 22. 3 km Twitter London Images generated in Overture by Keima
  • 23. 1.5 km Twitter London Images generated in Overture by Keima
  • 24. 0.8 km Twitter London Images generated in Overture by Keima
  • 25. 400 m High demand areas Twitter London Images generated in Overture by Keima
  • 26. Demand is complex: Road users; Pedestrians; Residential; Business; Railroads; Etc.
  • 27. 200 m Overall Demand (composite map) Images generated in Overture by Keima
  • 28. 200 m Indoor Demand Images generated in Overture by Keima
  • 29. 200 m Outdoor Demand Images generated in Overture by Keima
  • 30. What is the deployment focus? Macro / outdoors, continuous coverage; Macro / high mobility demand, highways; Small cells / outdoors, hotspots; Small cells + Wi-Fi / offload, POIs; Etc 200 m Outdoor Demand with Small Cells (light blue) and Wi-Fi + Small Cells (dark blue) Images generated in Overture by Keima
  • 31. Interference Continuum - Macro / Small Cell Power Difference Macros are 10 – 20 dB more powerful Distant macros can have a significant effect on small cells The “Super Cooper” Expansion Challenges 3 Interference It is important to deal with such an interference continuum by predicting the signal and interference across entire cities.
  • 32. Interference continuum Images generated in Overture by Keima Boston 8 km
  • 33. Boston Images generated in Overture by Keima 4 km
  • 34. Boston Images generated in Overture by Keima 1 km
  • 35. Boston Images generated in Overture by Keima
  • 36. Boston Images generated in Overture by Keima 250 m
  • 37. Boston Images generated in Overture by Keima 100 m
  • 38. Combinatorial Explosion – Complex interplay of dependent objectives. The “Super Cooper” Expansion Challenges 4 Cell planners will have to consider for each new cell during future 100,000+ rollouts: Location, configuration and technology parameters; Backhaul proximity and wireless clearance; Rental costs; Latency; Etc.
  • 39. 2012 N2012 ~ 290,000 Images generated in Overture by Keima Macros 500 km
  • 40. 2015 N2015 ~ 600,000 Images generated in Overture by Keima Macros + Small Cells (estimate) 500 km
  • 41. Cells will only be deployed in areas where there is a positive return on investment. Return on investment should consider: Sites that pay their way by locating near high demand “hotspots”; Sites with manageable interference impact; AND Backhaul or rental costs are affordable. Only by considering ALL objectives can we maximise return on investment: plan small cells holistically.
  • 42.
  • 43. • Automation seeks to maximise RoI by considering: – Optimal traditional towers; – Optimal utility poles; – Optimal wall mounting; – Suggesting search ring; – Backhaul costs; rental costs; – Etc.
  • 44. Example 1 • New York Case Study: – Low powered small cells (1W); – Street furniture: lighting fixtures, kiosks, power lines, etc.; – Primary and secondary attachments; – Wireless and fiber backhaul; – High spectral efficiency.
  • 45. Total candidate set: > 470,000 Small cells (1W) required: 1852 Primary location – green Secondary location – orange Manhattan Images generated in Overture by Keima 1 km
  • 46. All candidate locations are street furniture elements -> road alignment 0.5 km Manhattan Images generated in Overture by Keima
  • 47. 0.5 km Manhattan Images generated in Overture by Keima Number of cells follows demand
  • 49. Wireless @ 60 GHz 0.5 km Manhattan Images generated in Overture by Keima
  • 50. Wireless @ 10 GHz 0.5 km Manhattan Images generated in Overture by Keima
  • 51. Fibre Routes 250 m Manhattan Images generated in Overture by Keima
  • 52. 0.5 km Manhattan Images generated in Overture by Keima CINR 1 Mbps cell edge performance
  • 53. 100 m Manhattan Images generated in Overture by Keima
  • 54. Manhattan Images generated in Overture by Keima
  • 55. Location is key to maximise spectral efficiency Data accuracy more important than ever (1m resolution) Interference continuum Different environments and different cell types Non uniform demand Design focus is to maximise spectrum utilisation Demand clusters served by 64 QAM zone Automation Deployable business case depends on holistic network design The “Super Cooper” Expansion Conclusions
  • 56. Example 2 • London Case Study: – Low powered small cells (2 W) + Wi-Fi systems; – Street furniture: lighting fixtures, kiosks, power lines, etc.; – Wireless and fibre backhaul; – High spectral efficiency.
  • 57. Indoors Demand Outdoor Demand with selected Small Cells and Wi-Fi Small cell service area Wi-Fi service area
  • 58. 800 m NLOS Connections London Images generated in Overture by Keima
  • 59. 800 m 10 GHz Fresnel Connections London Images generated in Overture by Keima
  • 60. 800 m 60 GHz Fresnel Connections London Images generated in Overture by Keima
  • 61. London Images generated in Overture by Keima
  • 62. London Images generated in Overture by Keima
  • 63. Thank You Contact Us Iris Barcia iris.barcia@keima.co.uk Simon Chapman simon.chapman@keima.co.uk