ICON RESEARCH

1. PerceptionControlConnectivity
Wireless
Systems
Low-Latency Low-Latency
Machine
Learning
(ML)
Scalable
Reliable Reliable
Scalable
ML for communication
(MLC)
Communication for ML
(CML)
MLC/CMLE2EIntelligentSystems
criticactor
$obtain
AoI & reward
➀
train
policy NN
➁ train
Q NN
➃
schedule
uplink Tx
➂
time
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time
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time
⌧3<latexit 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sha1_base64="UXm6T+HBNCV9jNTbnAV7qnJYSyc=">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</latexit>
AoI
wind dynamics collision-safe separation
upload
local avg. logit/label
➁
select
teacher’s logit
➃( , )<latexit 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+
➂download
g oba avg. og t/ abe
store
oca
og t
➀
4 5 6 7 8 9 10
Number of Dev ces
105
106
107
108
10
9
10
10
10
11
SumCommuncatonCost(bts/epoch)
FL+FAug (IID)
FL (non-IID)
FD+FAug (IID)
FD (non-IID)
FD
FL
non-IID
+
+
⇠uu
queue
ength
t me
q0
⇠uu ⇠uu⇠
⇠uu
⇠uu
upload
oca parameters
➀download
g oba parameters
➁
control
Tx power
➂
q0
queue
ength
d str but on
GPD( , ⇠)
20 40 60 80 100
Total VUEs
10
0
10
1
10
2
Exchangeddata[kb]
0.9
0.99
0.999
0.9999
0.99999
ReliabilityPr(qq0
)
CEN
ExtFLX
Extreme Queue Length FL for Vehicular URLLC Power Control
Depth-Aided Mobility Prediction for mmWave Handover
• Next paths of vehicle user equipments (VUEs) are predicted using depth-camera
images, thereby proactively handing over to the mmWave BSs in LOS conditions
• Multiple vehicles’ paths are concurrently predicted using the same images
• Objective: Minimize uplink power while ensuring short queue length with high probability
• Following extreme value theory (EVT), an extremely large queue length is characterized
by the shape and scale parameters of the generalized Pareto distribution (GPD)
• Utilizing FL with EVT (ExtFL), VUEs collectively predict the GPD parameters
• ExtFL reduces communication overhead while achieving the same queue length reliability,
compared to a centralized direct queue length distribution exchanging baseline (CEN)
Federated Distillation (FD)
• FD exchanges local neural network (NN) model output for collectively training a global NN
• Compared to federated learning (FL) exchanging model parameters, FD yields 26x
smaller communication payload size, enabling distributed ML with deep NNs
Visuo-Haptic Perceptual Slicing based on Field-of-View (FoV) Prediction
Massive UAV Control via Mean-Field Game (MFG)
• Visuo-haptic VR traffic is supported via eMBB-URLLC links with OMA/NOMA
• URLLC is deactivated if haptic objects do not exist within the next predicted FoV
• Vehicles collectively build a dynamic map by exchanging local maps in real time
• The FoV and object resolutions of each local map are predicted via FD based on
vehicle-human perception and actuation ranges
0 1 2 3 4 5 6 7 8 9 10 11
106
0
1
2
3
4
5
6
7
8
9
10
107
OMA
OMA, s m.
NOMA
NOMA, s m.
NOMA w. power m t
NOMA w. power m t, s m.
NOMA
NOMA
w. power limit
OMA
0.1
MaximizedRateofLink2(nats/sec)R⇤
2,j
Target Rate of Link1 (nats/sec)ˆR1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
10-3
0 1 2 3 4 5 6 7 8 9 10 11
104
0
1
2
3
4
5
6
7
8
9
10
107
0
centra
contro er
w re ess y contro ed AI contro ed
Total VUEs
ExchangedData
ReliabilityPr(SNR>θ)
Without Predictions
GPS-Based
Image-Based
[1] J. Park, S. Samarakoon, and M. Bennis, “Wireless Network Intelligence at the Edge,” submitted to PIEEE
[2] E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim, "Federated Distillation and Augmentation under Non-IID Private Data,” presented at NeurIPS 2018 MLPCD
[3] S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications," submitted to TCOM
[4] J. Park and M. Bennis, “URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems,” in Proc. GLOBECOM 2018
[5] C. Perfecto, J. Del Ser, and M. Bennis, “Millimeter-Wave V2V Communications: Distributed Association and Beam Alignment,” JSAC, 2017
[6] H. Kim, J. Park, M. Bennis, and S.-L. Kim, "Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach,” in Proc. SPAWC 2018
[7] A. Elgabli, H. Khan, M. Krouka “Reinforcement Learning Based Scheduling Algorithm for Optimizing Age-of-Information in Ultra Reliable Low Latency Networks,” to be presented at ICC 2019
Vehicle-Human Perceptual Dynamic Map with FoV and Resolution Prediction
collect
depth snapshots
➀
predict
next path
➁
handover
to the BS n LOS cond t on
➂
b ockage
Multi-Sensor Control based on Age-of-Information (AoI) Real-Time Collaborative Robot Control
Total VUEs
+
posture data
current
FoV
federated
training
local FoV prediction
(accuracy: 80%)
next frame
eMBB
URLLC
1
augmented FoV
(accuracy: 99.999%)
3
5
deactivation
STOP haptic object detection
w th n the augmented FoV
4
2
FoV
h gh res.
+
federated
training
1
predict
FoV and reso ut ons
2
sensor data
ow res.
exchange oca maps3
16 8
13 4
3
18 2310
63
7
2
2122
5
10
1
17
Intelligent Connectivity
Assoc ate Prof. Mehd Benn s
Intelligent Connectivity and Networks
Jihong Park, Sumudu Samarakoon, Anis Elgabli, Mohammed S. Elbamby, Chen-Feng Liu, Trung Kien Vu, Hamid Shiri,
Mohamed K. Abdel-Aziz, Mounssif Krouka, Hamza Khan, Mojtaba Jahandideh, Abanoub M. Girgis, and Mehdi Bennis
Centre for Wireless Communications, University of Oulu, Finland