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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

Complexity of the Search

Complexity of the Search N3 X N2

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)

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.

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!

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.

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.

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

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

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

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

Groundtruth Data Collection SkyRAN UAV in Action Groundtruth Data Collection

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

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.

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