The mobile computing lab is led by Professor Li-Chun Wang whose research interests include 5G wireless communications, data-driven learning for radio resource management, and big data analytics for industrial IoT. The lab has partnerships with IBM Watson for stream computing education and research and has received funding from IBM to support an openflow-based datacenter testbed with servers, switches and controllers donated by Extreme Networks. The lab is currently working on several projects applying AI techniques to problems in drone communications networks, industrial IoT latency and reliability, and predictive maintenance for numerical control machines.

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Mobile Computing Lab
 Chair Professor, Li-Chun Wang ( 王 君蒞 )
 Research Interests:
 5G Wireless and Mobile Communications

Data-Driven Learning-Assisted Radio Resource Management
 Big Data Analytics for Industrial Internet of Things (IIoT)
 AI-enabled UAV
 Honors
 IEEE Fellow (2012)
 MOST Outstanding Research Award (2012, 2018)
 Best Paper Award: IEEE COMSOC APB (2015); IEEE VT
Jack Newbauer

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intelligent Internet of Things (iIoT)
Social
Mobile Cloud
Big-Data Analytics
Security
Better Healthcare  Safer Planet
Information

3. 3交通大學電機所
IBM Streams Computing for i-IoT
∗ Cooperation with IBM Watson in promoting “Stream Computing and Big Data
Analysis” research and education
∗ Summer Training course have been offered by the experts of IBM Watson for 6
years.
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Zion
Server
Extreme
OpenFlow
Switch
OpenFlow
Controller
NCTU OpenFlow-based
Datacenter Testbed
 Zion Servers
 70 Nodes
 560 Cores (4Core/CPU;2CPUs/Node)
 3.36TB Memory
 140TB HD
 Emulate 490~2000+
VMs
 Extreme OpenFlow Switch x 8
 NOX OpenFlow Controller
Machine Machine Machine….
OpenFlow Management Network
OpenFlow
Controller
Production NetworkSoftware-Defined Network
英業達捐贈價 ：值 727 萬

5. 5
A Book for 5G
5

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5G Vertical Markets
MVNO DMVNO D
MVNO: Mobile Virtual Network Operator
MVNE: Mobile Virtual Network Enabler
MVNO CMVNO C MVNO BMVNO B MVNO AMVNO A
Carrier2
Carrier1
Public Network
Factory of
the Future
VR/AR
Stadium
Tele-Drone
Fleet
SI(B)
Enterprise
Private Network
VerticalServiceProvider
MEC ( vEPC + vAPP )
MVNE
Licensed
Spectrum
Licensed
Spectrum
Shared
Spectrum
Smart
Grids
Courtesy of Taiwan 5G Office

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Research Projects
1. Intelligent Communications for Drone-Cruiser Cellular Networks
2. Latency-Aware Industrial IoT
3. 5G Ultra-reliable and Low Latency Communications
4. Industrial Big Data Application Research

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Intelligent Communications for
Drone-Cruiser Cellular Networks
 Project Goal
 Develop a new UAV, Drone Cruiser, as the new
flying base stations and apply AI techniques for
solve the complex issues on dynamic factors in 5G
beyond cellular systems
 Develop a 3D communications QoS prediction
platform to help the dynamic placement of UAV-BSs
 Create more innovative services and applications

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Intelligent Communications
for Drone-Cruiser Cellular Networks
 Issues:
 Limited hover time for current drone
 Learning-based Air-to-Ground Wireless Channel
Modeling
 3D Placement of Drone Base Stations
 Network Integration and Handover Techniques for
the joins and departures of Drone Base Stations
 Development of Innovative Services The developed techniques can be
applied to:
•Aviation
•5G communications industries
•Industry 4.0,
•3D Internet of Things,
•cloud big data services,
•intelligent control, and other fields
ApplicationsApplications
 Solution:
 Data-driven optimization and AI-based
prediction for the management of UAV
base stations with real 3D wireless channel
data

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Project Architecture
Intelligent
Communications and
Networking
Technologies for
Drone-Cells
Data Science & Analysis
Prof. Wang
Prof. Lin, Prof. Deng,
Prof. Chang
Prof. Wang, Prof. Lin,
Prof. Shuai
Prof. Shuai, Prof. Huang
PI: Prof. Wang
Industrial Partners
GEOSAT
Chunghwa Telecom
AI UAV-BS Communications
System Integration & Innovation
Application Service Development
AI UAV-BS Network and 3D Placement
Innovative Carrier Design of UAV-BS Prof. Cheng, Prof. Li

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Drone Cruiser
Designed by Prof. Teng-Hu Cheng, Mech. Engr. of NCTU

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Learning-Based Radio Maps
 3D Channel Radio Map
 Combine with some open 3D maps
 QoS prediction tool/platform for UAV-assisted
cellular networks
LoS: Line of Sight
NLoS: Non Line of Sight

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Application Scenarios
Edge Computing
• 3D Placement Service
• Precomputed
Recommendation
• Offline Learning
• Reinforcement
Learning
Backhaul
• mmWave
• Beamforming
• Trajectory planning
Environmental Sensing
• Location & Mobility Pattern
Recognition
• Environmental Feature Extraction
• 3D Channel Modeling
Resource Management
• Displacement of UAV-BS
• Online learning
QoS Analysis
• Real-time
monitoring

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UAV-Assisted Cellular System
14Macro-cell
FBS cell #1
FBS cell #2
FBS cell #3
FBS cell #4
Resource for FBS cell #1 (UTLLC)
Resource for FBS cell #2-#4
(eMBB)Resource for Marco-cell
Resource dedicated to FBS
URLLC eMBB
D2D/FANET Communication
FBS backhaul Link (mmWave)
Traditional Cellular Link
a. Learning-based 3D Drone-BS Placement for Bandwidth and Power Allocation (Trent)
b. Data-driven 3D Placement of UAV-BSs for Mitigating Inter-drone Cell Interference ( 俊廷 )
c. Learning-based Trajectory Planning for Always Connected FBS ( 郁芳 )
d. Modeling and Analysis of UAV-Assisted Heterogeneous Cellular Systems for Ultra-reliable
and Low Latency Communications (Allan)
e. Power Control for Uplink Multiplexing of URLLC and eMBB in 5G Networks ( 晨曜 )
f. Modeling and Analysis the Delay and Reliability of D2D Communication in a UAV Swarm
with URLLC Requirement (Irene)
g. Unified Offloading Analysis Model of Multi-access Edge Computing and Communications
for UAV Network (Jim)
① ②
③
④
FBS cell #5
⑤
⑥
Edge Service
⑦

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 Challenge: The existing transmission mechanism can not reach the strict delay
requirements of IIoT
 Network resources are limited
 Traffic characteristics change dynamically
 Approach: Delay-aware group-based MTC scheme
 Adaptively adjust aggregation buffer size based on the change of real-time traffic
 Adaptive cluster partition scheme by bin packing based on delay performance
 Gain:
 [Uplink transmission latency] 303 ms → 113 ms (↓62%)
5G Machine Type Communications for Latency-Aware Industrial
Internet of Things (IIoT)
303 ms303 ms
113 ms113 ms
62%62%
Move
Reliable Device-
aggregator Associations
Fixed Aggregator
Moving
Aggregator
IIoT TestbedIIoT Testbed

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 Challenge: The existing 802.11ad link setup latency
can not reach the AR/VR requirement.
 802.11ad link setup latency: >40 ms
 AR/VR latency requirement: ~ 4 ms
 Approach:
 AI-based light-weight WiFi fingerprint beam search
 Gain:
 [Beam search latency] 10.95 ms → 1.09 ms (↓90%)
 [Probability of availability] 89% → 98% (↑11%)
Low-Latency High-availability 5G Indoor Communications
Training Trajectory
Signal Mapping
Ideal Practical

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UPER scheme
Almost achieve
URLLC requirement
Delay requirement (1 ms)
 Challenge: How to avoid resource collision between urgent uplink URLLC
with other services (e.g., eMBB or mMTC)?
 Approach:
 User-initiated probabilistic elastic reservation (UPER) scheme

Probabilistic resource selection between dedicated and shared resource blocks.

K-repetition transmit in frequency domain.
 Gain: (For 0.25 ms grant-free transmission interval)
 [Average delay time] 0.56 ms → 0.385 ms (↓31%)
 [1 ms delay reliability] 85.59% → 99.994% (↑14.404%)
Resource sharing for 5G URLLC Cellular Systems
UPER
others
URLLC
Unreserved
others
URLLC
URLLC dedicated resources
Shared resources
URLLC
UL traffic
eMBB/mMTC
UL traffic
Resource blocks
Reserved resources
UL UL UL UL UL UL UL
0.25 ms
URLLC:
Ultra Reliable Ultra Low
Latency Communications

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 Motivation: The cost of shutting machines down for one month to repair is
about NT 6.8 million. The products produced by failed machines are also big
cost. A predictive maintenance model to notify maintenance before machine
fail is necessary.
 Challenge: The error of existing anomaly detection model by RNN increases
a lot and costs much computation time since the sensor data of numerical
control machine is regression data.
 Approach: An end-to-end non-linear value mapping model for anomaly
detection.
 Gain:
 Map regression data to classification data.
 Increase accuracy.
 Training time speed up 16 times
Real-Time Data Analytics and Predictive Maintenance Platform for
Numerical Control Machine

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實驗室理念
學生為本，創意為先，以終為始，莫忘初衷
Learning
Optimistic
Value
Everlasting
歡迎 們你 !

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Cooperation Partner/Funder