1. SkyRAN: A Self-Organizing LTE RAN in the Sky
Ayon Chakraborty, Eugene Chai, Karthik Sunderesan
Amir Khojastepour, Sampath Rangarajan
Mobile Communications and Networking Group
NEC Labs America
ACM CoNEXT 2018
Heraklion, Greece

2. Disconnected by Disaster
People desperately trying to reach out for cellphone signal after the area was hit by Hurricane Maria in Dorado, Puerto Rico.

3. UAV networks useful for on-demand connectivity
Disconnected by Disaster
Short-term, on-demand connectivity solutions over limited geographic regions where additional capacity or coverage is needed.
People desperately trying to reach out for cellphone signal after the area was hit by Hurricane Maria in Dorado, Puerto Rico.

4. SkyLiTE: Beaming LTE from Sky
Vision: On-demand, highly flexible, low-altitude UAV communications when fixed infrastructure is not available
Data
Control
Macro eNB
Control Station

5. SkyLiTE: Beaming LTE from Sky
SkyHAUL
SkyHAUL
SkyCORE
SkyCORE
[Mobicom’18]
SkyRAN
SkyRAN
[CoNEXT’18]
Macro eNB
Control Station

6. Principles Apply to WiFi as well
SkyFi
SkyHAUL
SkyHAUL
SkyRAN
SkyRAN
Macro eNB
Control Station

7. SkyRAN: Optimizing the Access Network
SkyLiTE
SkyHAUL
SkyHAUL
SkyCORE
SkyCORE
[ACM Mobicom’18]
SkyRAN
SkyRAN
[ACM CoNEXT’18]
Macro eNB
Control Station

8. Where to position the UAV in 3D space?
Search Space
Lacks Coverage
Good Connectivity = Good Coverage + Good Capacity
Good Capacity

9. Where to position the UAV in 3D space?
Search Space

10. Where to position the UAV in 3D space?
Good Coverage
Search Space
Poor Capacity
(shadowing)
Moderate
Capacity

11. Where to position the UAV in 3D space?
Search Space

12. Where to position the UAV in 3D space?
Good Coverage
Search Space
Good Capacity
Poor Capacity
(shadowing)

13. Where to position the UAV in 3D space?
Search Space
… and similarly, …

14. Where to position the UAV in 3D space?
Good Coverage
Search Space
Finally, …
Good Capacity
Optimal UAV position?

15. Where to position the UAV in 3D space?
Good Coverage
Good Capacity
Optimal UAV position?

16. Complexity of the Search

17. Complexity of the SearchN3 X N2

18. Needle in the Haystack Constraints
How do we systematically search for the optimal operating point?
Constraints
UAV has limited battery power
Maximize the duration the UAV offers optimal connectivity (capacity + coverage)

19. SkyRAN’s Approach in a Nutshell
Estimate the Radio Environment Map
(REM) per UE in the network. Channel state information more fundamental.
UE 1
UE 2
UE 3
f(.)
Compute an aggregate REM for all UEs in the network. The aggregating mechanism depends on a utility function of choice, f(.).
argmaxf(loc)
Place UAV at a location that maximizes the utility function, in the aggregated map.

20. Estimating the REM UEs Localize the UE(s)
ToFN
ToF2
ToF1
UEs
(X, Y)
Localize the UE(s)
LTE Uplink SRS symbols are used to compute Time-of-Flight (ToF).
The UAV’s trajectory forms a synthetic aperture that is used to multilaterate for the UE location.
SkyRAN achieves a median localization accuracy of about 5 – 7 meters!

21. Estimating the REM Operating Altitude Selection
Find the optimal operating altitude for the UAV.
Track the LTE pathloss while the UAV climbs up vertically. The pathloss shows a (local) minima at a given altitude.
Note that this is not globally optimal but reasonably closer.

22. Estimating the REM
REM estimated by using a simple pathloss model. No knowledge about terrain.
Locations with high signal gradients are clustered. Signal gradients implicitly encode information about terrains.
A minimum distance trajectory (TSP) is constructed joining the cluster centers.

23. Estimating the REM
REM estimated by using a simple pathloss model. No knowledge about terrain.
Locations with high signal gradients are clustered. Signal gradients implicitly encode information about terrains.
A minimum distance trajectory (TSP) is constructed joining the cluster centers.
REPEAT

24. Estimating the REM
REM estimated by using a simple pathloss model. No knowledge about terrain.
Locations with high signal gradients are clustered. Signal gradients implicitly encode information about terrains.
A minimum distance trajectory (TSP) is constructed joining the cluster centers.
REPEAT

25. Estimating the REM
Repeated till further sampling doesn’t add significant information or flight-time budget is finished.
REM estimated by using a simple pathloss model. No knowledge about terrain.
Locations with high signal gradients are clustered. Signal gradients implicitly encode information about terrains.
A minimum distance trajectory (TSP) is constructed joining the cluster centers.
REPEAT

26. Overall SkyRAN Operation
Monitor UE Performance
POOR
GOOD
Localization
REM
GPS
Multilateration
Opt. Altitude
LTE Service
Optimal
Position
EPOCH
Trajectory
LTE SRS
TOF
Update REM

27. Groundtruth Data Collection
SkyRAN UAV in Action
Groundtruth Data Collection

28. Not only the area but the terrain complexity impacts significantly.
Performance
Improvement of relative throughput with more flight time.
Time to reach 0.9× throughput w.r.t. that at the optimal position
Not only the area but the terrain complexity impacts significantly.
Diminishing Returns
250m x 250m
250m x 250m
1Km x 1Km

29. Salient Points
SkyLiTE: First of it’s kind system that provides LTE connectivity using UNTETHERED UAVs.
SkyRAN adapts to changes in overall UE locations to maintain connectivity.
SkyRAN preserves historical information for instantiating REMs for future UEs that move to the same or similar location.
Achieves 0.9 – 0.95X of the optimal throughput with moderate flight time budgets.

30. Thank You Ayon Chakraborty ayon@nec-labs.com ACM CoNEXT 2018
Heraklion, Greece