learning from the sky - cnit
TRANSCRIPT
Learning from the Sky:Robot-Aided Mapping, Radio Access
and Localization
WiLab-Huawei Workshop Jan. 2021
David Gesbert
EURECOM, Sophia-Antipolis, France
Collaboration with Omid Esrafilian@EURECOM,
Rajeev Gangula@EURECOM, Junting Chen@USC, U. Mitra@USC
UAV-aided networks: Use cases for micro-drones
Hot-spots, sport
events, flashcrowds Range extension
Disaster recovery
IoT data harvesting, smart
city, agriculture, caching
D2D connectivity: internet connectivity,
car2car connectivity for assisted driving, mesh
connectivity, battlefield connectivity,…
Reliable robotic UAV placement
-
Initial UAV position
Optimal position
Learning trajectory
Optimal path
Can integrate local/instantaneous features (vs. probabilistic placement)
Relying on radio sensing capabilities Position vs. path planning
On-line vs off-line
Finite user population vs. fluid models
Obstacle avoidance & navigation
Probabilistic vs map-based prediction
Probabilistic (LoS) prediction
Ex: LoS probability model
11 January 2021 5
[Hourani14] A. Al-Hourani, S.
Kandeepan, and S. Lardner, “Optimal
LAP Altitude for Maximum
Coverage”, IEEE Comm. Lett., 2014.
Map-based prediction
Segmented Channel Path Loss Models
Class s=1,2,3,..
Av. RX power
Path loss exponent
shadowing
Fixed offset
distance
Received power map UAV 100
meter above center of Bristol
ML-based Radio Map Reconstruction
APPROACH 1:
Classical ML
(KNN applied to
RSSI-domain image)
APPROACH 2:
Model-based ML
(Segment Classification
Followed by KNN )
[J. Chen & D. Gesbert, 2017]
Expert knowledge is important!
K nearest neighbors (KNN) with Kernel:
KNN applied to RSSI image
(no channel model)
KNN applied to
model-classified data
(hard/soft reconstruction)
Question: How to scale with #users?
3D Reconstruction
1km
Reconstructed map with 1500 users*UAV trajectory
800 m
*K=32 UAV locations, spatial smoothing applied
Optimized flying altitude in closed form (Globecom 17)
Joint 3D and radio map reconstruction
10
An orthoimagery of
an area at center
Washington DC, USA
[Chen, Esrafilian, Gesbert, Mitra, Robotics, Science and Systems, MIT, 2017]
Radio map
reconstruction
Radio map estimate
3D map estimate
Joint approach
Trajectory design use cases
Scenario 1: UAV as cellular relay
Scenario 2: “Smart” IoT data harvesting
Scenario 3: UAV-aided mesh connectivity
Map information can be too much information !
Single user-drone radio map
UAV as an autonomous cellular relay (live demo @ 2.5GHz)
https://www.youtube.com/watch?v=GI_lOsg_qmQ
UAV (EURECOM)
User (off-the-shelf phone)Base Station (EURECOM)
Scenario 2: IoT “data harvesting”
[Esrafilian, Gangula, Gesbert, IEEE Journal IoT, 2019]
Problem: Find path and schedule which harvests the “most
data” from ground nodes, under fixed flying time
Assumes map knowledge
But problem not differentiable
“Map compression”: Local probabilistic model
-> to make problem differentiable
-
• User
Global LOS Probability model:
Local map-aided LOS Probability:
Map-based Trajectory design & User Scheduling
-
Shadowing
Fixed offset
Path loss exponent
Scenario 3: UAV-aided Mesh connectivity
- - p 17
•Previous work: Morgenthaler, Wi-UAV 2012, Yanmaz., et al WCNC 2014, PIMRC 2015, etc.
•However, no optimal UAV placement!
Scenario 3: UAV-aided Mesh connectivity
- - p 18
• Challenge: Optimal placement depends on routing algorithm (OLSR..)
• Two Phase approach:
• Clustering of nodes
• UAV placement to optimize inter-cluster connectivity.
• Placement:
Where:Average path-loss Transmit power
Number of nodes in cluster k
• This problem is again solved with map compression -> SCA can be used!
Outline
UAV placement and path design
Channel prediction
Learning maps
Communication trajectory design
Trajectories design with Active Learning
Perspectives
Active Learning #1: Learning 3D building Maps
NMSE = 0.2488
Arbitrary Paths
Dynamic Programming-Optimized Path
NMSE = 0.343 (averagr across arbitray paths)
Refinement
Graph
[Esrafilian, Gesbert, 2017]
Active Learning #2: Learning the channel
Goal: design a flight path to estimate channel
parameters with minimal error variance -> DP
[Esrafilian, Gangula, Gesbert, sub IEEE Journal IoT, 2018]
Active Learning #3: User localization
• Flight goal:
• Collect RSSI measurements from K users
• To learn the channel parameters and localize the users
• Leveraging the 3D map
• User localization:
• PSO (particle swarm opt)
• Trajectory design:
• Active Learning based on
Fisher Information matrix.
[O. Esrafilian, R. Gangula, D. Gesbert, "3D Map-based Trajectory Design in UAV-aided Wireless Localization
Systems", submitted to the IEEE Internet of Things Journal, April 2020]
Perspectives
Coordination across multiple UAVs
Low complexity algorithms (DP is complex!)
Onboard RF/antenna design
Onboard vs. offboard computing
Advanced mix robotics-communications models
Fusion of radio and vision/LIDAR data ([Esra Gang Gesb 2021])
All references under www.eurecom.fr/cm/gesbert
Plenty of hard problems (theoretical/experimental)
UAV-aided networks: Promising technology Makes the network flexible, closer to the end user
Comm-robotics interactions (mapping..)
Side benefits: User localization, reliable flying terminal,..