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국방 NCW 포럼 특별세션
UAV 활용 무선통신 기술
2017. 11. 29
Jae-Hyun Kim
Wireless Internet aNd Network Engineering Research Lab.
http://winner.ajou.ac.kr
School of Electrical and Computer Engineering
Ajou University, Korea
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Contents
Introduction
Research Trends for UAV-aided wireless communication
Research Interest
Conclusion
2
1
2
3
4
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Introduction
3
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Introduction
UAV(Unmanned Aerial Vehicles)
Definition [1]
Aerial vehicles that do not carry a human operator can fly autonomously or be piloted remotely
Different types of aerial objects/systems [2]
Include drones(ex. quadcopter), HAP(High Altitude Platform), LAP(Low Altitude Platform), Balloons, etc
• HAP : 15 Km(altitude), 38 – 39.5 GHz (Frequency band - Global)
• LAP : between 200 m to 6 km
[1] Joint Publication 1-02, “DOD Dictionary of Military and Associated Terms.”
[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017[3] Airbus, “Zephyr, High Altitude Pseudo-Satellite”[4] Google, “Loon Project”, https://x.company/projects/loon/
4
<HAP(Zephyr)> <LAP(Predator)> <Balloon(Loon Project)>
<Drone>
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Introduction
UAV application & market growth
Application
Agriculture, transport, monitoring, patrol,entertainment, search and rescue, communications, etc.
Market growth[5]
[5] HIS Markit, http://news.ihsmarkit.com/press-release/aerospace-defense-security/significant-global-demand-pushes-uav-sales-exceed-82-billio 5
Agriculture
Entertainment Transport
MonitoringCommunication※ CAGR(Compound Annual Growth Rate)
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Introduction
UAV-aided Wireless Communication
Functions of UAV in wireless communication
Communication among UAVs
• FANET(Flying ad-hoc Network), UAV swarm
Communication relay nodes
• Connect disconnected MANET(Mobile ad hoc network) clusters
Network gateway
• Connectivity to backbone networks, Internet, etc.
Advantage
Rapid placement
Flexible and scalable deployment
Coverage expansion
Low-cost operation
[6] I. Jawahr, N. Mohamed, J. A. Jaroodi, D. P. Agrawal, S. Zhang, “Communication and networking of UAV-based Systems: Classification and associated architectures,” Journal of Network and Computer Application, vol. 84, pp. 93-108, Apr. 2017 6
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[7] KT, https://corp.kt.com/html/biz/services/trial.html [8] AT&T, “When COWs Fly: AT&T sending LTE Signals from drone,” http://about.att.com/innovationblog/cows_fly[9] Verizon, http://www.verizon.com/about/news/first-responders-make-calls-and-send-text-messages-using-flying-cell-site[10] SKT, http://www.sktelecom.co.kr/advertise/press_detail.do?idx=4190
Case of UAV-aided Wireless Communication(Commercial)
Mobile base station
KT(2015)
AT&T(Flying COWs, 2017)
Verizon(Flying cell site, 2016)
NTT DoCoMo(2017)
PS-LTE(Public Safety LTE)
SKT(control, 2017)
KT(Traffic Control Platform, 2017-2021)
Introduction
7
<KT> <AT&T, Flying Cow>
<PS-LTE><SKT, PS-LTE>
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[11] Google, Project Skybender, https://www.theguardian.com/technology/2016/jan/29/project-skybender-google-drone-tests-internet-spaceport-virgin-galactic[12] Intel, https://www.intel.com/content/www/us/en/drones/drone-applications/commercial-drones.html[13] China mobile, https://www.sdxcentral.com/articles/news/china-mobile-eyes-5g-enabled-drones-solve-network-latency/2016/08/[14] IBM Watson, https://www.ibm.com/watson/
Case of UAV-aided Wireless Communication(Commercial)
5G mobile communication
Google (Skybender project, 2016)
China Mobile(2016)
UAV-based IoT platform
Intel(2016)
Introduction
8
<Intel> <China mobile>
<Google><IBM>
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Case of UAV-aided Wireless Communication(Military)
Integrated tactical Network
JALN(Joint Aerial Layer Network)
• DoD
MUSIC(Manned Unmanned System Integration Capability)
• US Army
Multi-layer UAV network
ASIMUT Project
• European Defence Agency(EU), THALES, Bordeaux Univ., Luxmbourg Univ., Fraunhofer IOSB, Fly-&-Sense
Introduction
9
<ASIMUT project>
<JALN>
[15] “Joint Concept for Command and Control of the Joint Aerial Layer Network”, Joint Chiefs of Staff, 2015.03[16] U.S Army, “Manned Unmanned Sytems Integration Capability: MUSIC”[17] ASIMUT, https://asimut.gforge.uni.lu/description.html
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[18] ‘Office of Naval Research’, https://www.onr.navy.mil[19] ‘U.S. Departure of Defense,’ https://www.defense.gov
Case of UAV-aided Wireless Communication(Military)
UAV swarm
Perdix-micro UAV swarm
• Department of defense(USA), MIT
• Field Test : Oct. 2016
LOCOST program
• Office of Naval Research(USA)
• Field Test : Apr. 2015
Introduction
10
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Introduction
Design challenges of UAV-aided wireless communication
Ensuring reliable network connectivity
High mobility environment of UAV systems
• Sparsely and intermittently connected
Effective interference management techniques
Mobility of UAVs, the lack of fixed backhaul links and centralized control
• Interference coordination among the neighboring cells with UAV-enabled Aerial base station
Energy-aware UAV deployment and operation mechanism
Limit UAVs communication, computation, and endurance capabilities
• SWaP(size, weight and power) constraint
Effective resource management and security mechanism
Supporting safety-critical functions(ex. CNPC links)
• Stringent latency(real-time) and security requirements
11[20] Y. Zeng, R. Zhang, T. J. Lim, “Wireless Communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communication Magazine, vol. 54,
no. 5, pp. 36 – 42, May. 2016.
※ CNPC : Control and Non-payload Communication
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Research Trends for UAV-aided wireless communication
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A2G(Air-to-ground) Channel Model
A2G Channels typically include,• LoS(Line-of-Sight), NLoS(Non Line-of-Sight)
• Multi-path components
» Reflection, scattering, diffraction
A2G radio propagation over urban environment[21]
Excessive pathloss(𝜂𝑛)
Dominant components
• LoS : Strong Signal (exist with probability 𝑷)
• NLoS : Strong reflection, fading (exist with probability 1-𝑃)
the effect of small-scale fluctuations are not consider
13
[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017[21] A. A. Hourani, S. kandeepan, A. Jamalipour, “Modeling Air-to-Ground path loss for low altitude platforms in urban environments,” in proc.
Globecom 2014, Austin, TX, USA, Dec. 2014.[22] A. A. Hourani, S. Kandeepan, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Letters, vol. 3, no. 6, pp 569 – 572, Dec. 2014
LoS NLoS
Research Trends for UAV-aided wireless communication
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A2G(Air-to-ground) Channel Model
LoS probability over urban environments[22]
Dependent
• Ratio of built-up land area to the total land area(𝛼)
• Mean # of buildings per unit area(𝛽)
• Building’s heights distribution(𝛾) according to Rayleigh
ITU recommendation document suggests
LoS probability approximation• A continuous function of elevation angle 𝜽
» Closed sigmoid function
» 𝑟 = ℎ/𝑡𝑎𝑛𝜃, ℎ𝑅𝑋 0, smooth for large values of ℎ14
[22] A. A. Hourani, S. Kandeepan, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Letters, vol. 3, no. 6, pp 569 – 572, Dec. 2014
• 𝑎, 𝑏 : constant that depend on the environment• 𝜃 : elevation angle
Antenna height
𝑚 = floor(r 𝛼𝛽 − 1)
※ FSPL : Free Space Path Loss
Research Trends for UAV-aided wireless communication
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Research Trends for UAV-aided wireless communication
A2G(Air-to-ground) Channel Model : Cell Radius vs. LAP altitude
15[22] A. A. Hourani, S. Kandeepan, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Letters, vol. 3, no. 6, pp 569 – 572, Dec. 2014
*Urban environment
- PlMax : Maximum allowed pathloss
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A2G(Air-to-ground) Channel Model
Shadowing model for HAP in built-up areas[23]
Additional Shadowing Loss
• The shadowing effects of buildings on NLoS Connections
Rician fading model
Small scale fading
• Presence of a strong LoS component
K-factor[24]
• NASA measured in a near-urban
environment for CNPC link
» C-band(5.06 GHz) : Avg. 27dB (Min. 12.3dB)
» L-band (968MHz) : Avg. 12.7dB (Min 5dB)
16
• 𝐿𝐹𝑆𝐿 : Free space loss• 𝐿𝑆 : random shadowing in dB • 𝜁𝐿𝑂𝑆 , 𝜁𝑁𝐿𝑂𝑆 : random component
[23] J. Holis, P. Pechac, “Elevation dependent shadowing model for mobile communications via high altitude platforms in built-up areas,” IEEE Trans. Antennas and propagation, vol. 56, no. 4, pp. 1078 – 1084, Apr. 2008.[24] D. W. Matolak, R. Sun, “Air-Ground channel characterization for unmanned aircraft systems: the near-urban environment,” in Proc. MILCOM 2015,
Tempa, FL, USA, Oct. 2015.
Research Trends for UAV-aided wireless communication
𝑝𝜉 𝑥 =𝑥
𝜎02 exp(
−𝑥2−𝜌2
2𝜎02 )𝐼0(
𝑥𝜌
𝜎02)
• 𝜎02 : Average multipath component power
• 𝜌 : LoS amplitude• 𝐼0 : Bessel function
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UAV deployment
UAV deployment and path planning
UAV-aided cellular coverage application
• Main design problems to achieve maximum coverage
» The optimal UAV separations
» The optimal altitude
UAV deployment and Operation
Energy-efficient communication
• Aims to satisfy the communication requirement with the minimum energy expenditure on communication-related function
» Communication circuits, signal transmission, hovering time etc.
» Optimize the energy efficiency in 𝑏/𝐽(bit per Joule)
• Extensively studied for terrestrial communications
» IoT devices
17
Research Trends for UAV-aided wireless communication
[20] Y. Zeng, R. Zhang, T. J. Lim, “Wireless Communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communication Magazine, vol. 54, no. 5, pp. 36 – 42, May. 2016.
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UAV deployment
Next generation tactical communication networks with space and aerial
Purpose
• Future military communication network target service proposal including Aerial communication relay after TICN power-up
Network architecture
• By considering traffic size, mission and communication system
» Satellite(commercial, MILSAT, etc)
» UAV(High capacity, Low capacity)
» Ground(TICN, Solider)
18
Research Trends for UAV-aided wireless communication
[25] 조준우, 오지훈, 이재문, 김동현, 김재현, “우주/공중 기반 기동통신망 핵심기술, “한국통신학회지(정보와 통신), 제 33권 11호, pp. 65 – 72, 2016년 11월
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UAV deployment
Next generation tactical communication networks with space and aerial
Analysis of traffic load amount of High Altitude UAV
• Worst/Best : most/least ground nodes are connected ground station which are failed
• # of Ground station failure : 1 6
19
Research Trends for UAV-aided wireless communication
[25] 조준우, 오지훈, 이재문, 김동현, 김재현, “우주/공중 기반 기동통신망 핵심기술, “한국통신학회지(정보와 통신), 제 33권 11호, pp. 65 – 72, 2016년 11월
Tra
ffic
load
Tra
ffic
load
Rank of High Altitude UAV according to traffic load
Rank of High Altitude UAV according to traffic load
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UAV deployment
Deployment strategies of multiple UAVs for optimal wireless coverage[25]
Purpose
• Investigate the optimal 3D deployment of multiple UAVs in order to maximize the downlink coverage performance
» Derive the downlink coverage probability for a UAV as a function of the UAV’s altitude and the antenna gain
» Propose an efficient deployment method which leads tothe maximum coverage performance while ensuring thatthe coverage areas of UAVs do not over lap
Coverage range of each UAV
20[26] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEE
Communications Letters, vol. 20, no. 8, pp. 1647-1650, Aug. 2016.
Research Trends for UAV-aided wireless communication
• 𝑟 : arbitrary range• 𝑃𝐶𝑂𝑉 : coverage probability (using LoS probability)
• 𝜃𝐵 : directional antenna half beamwidth
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UAV deployment
Deployment strategies of multiple UAVs for optimal wireless coverage[25]
Maximize the total coverage
21
Research Trends for UAV-aided wireless communication
Approach Circle packing problem
[26] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEE
Communications Letters, vol. 20, no. 8, pp. 1647-1650, Aug. 2016.
R = 5km
Coverage vs. life time tradeoff : power, # of UAV, Altitude
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UAV Resource Management
Bandwidth requirement
Reason
• Variety of data types and requirements in each UAV
• Assigned the bandwidth of the aircraft system to the CNPC links
Design consideration
• Maximizing bandwidth efficiency while meeting the demands
Hover and flight time constraint
Reason
• Limited on-board batteries, Flight regulation, weather conditions, etc.
Design consideration
• Minimizing flight time while meeting the demands
• Optimizing the service performance under flight time constraints
22[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017
Research Trends for UAV-aided wireless communication
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UAV Resource Management
Dynamic Resource Allocation Algorithm
Purpose
• Propose frame structure and the resource allocation algorithm which can maximize the network throughput
» Satisfy the minimum data rate requirement
23[27] H. R. Cheon, J. W. Cho, J. H. Kim, “Dynamic resource allocation algorithm of UAS by network environment and data requirement,” in proc.
ICTC 2017, jeju, Korea, 18 - 20, Oct. 2017.
Research Trends for UAV-aided wireless communication
자원 할당 알고리즘
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UAV Resource Management
Dynamic Resource Allocation Algorithm
Algorithm
• Critical : Control message
• Uncritical : video, voice, etc.
24
Research Trends for UAV-aided wireless communication
①
②
③
④
[27] H. R. Cheon, J. W. Cho, J. H. Kim, “Dynamic resource allocation algorithm of UAS by network environment and data requirement,” in proc. ICTC 2017, jeju, Korea, 18 - 20, Oct. 2017.
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UAV Resource Management
Dynamic Resource Allocation Algorithm
Performance result
• Compared Fixed unit time slot and Dynamic time slot
» Fixed : 1ms
25
Research Trends for UAV-aided wireless communication
[27] H. R. Cheon, J. W. Cho, J. H. Kim, “Dynamic resource allocation algorithm of UAS by network environment and data requirement,” in proc. ICTC 2017, jeju, Korea, 18 - 20, Oct. 2017.
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UAV Resource Management
Optimal transport theory for hover time optimization[28]
Purpose
• Maximize the average number of bit(data service) that is transmitted to the users under a fair resource allocation scheme
• The minimum average hover time that the UAVs need for completely servicing their ground users is derived
26[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vehicles (UAVs): optimal transport theory for
hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
Research Trends for UAV-aided wireless communication
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UAV Resource Management
Optimal transport theory for hover time optimization[28]
Optimal cell partitioning for data service maximization with fair resource allocation
• Each cell partition is assigned to one UAV
27
Average data service at location (𝑥, 𝑦) ∈ 𝐴𝑖
• : cell partition• 𝑖 : # of UAVs • 𝑇𝑖 : effective transmission time of UAV 𝑖• 𝐵𝑖 : Bandwidth allocated to the user• 𝛾𝑖 : SINR
The load of each cell partition
Average # of users within
each cell partition
[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vhicles (UAVs): optimal transport theory for
hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
Research Trends for UAV-aided wireless communication
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UAV Resource Management
Optimal transport theory for hover time optimization[28]
Optimal cell partitioning for data service maximization with fair resource allocation
• Using Kantorovich Duality Theorem
» minimizing total transportation costs
• Unconstrained maximization problem
28
where
Cost function depending on data service
[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vhicles (UAVs): optimal transport theory for
hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
Research Trends for UAV-aided wireless communication
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UAV Resource Management
Optimal transport theory for hover time optimization[28]
Optimal cell partitioning for data service maximization with fair resource allocation
29
• 𝜎0 : distributed ground users(standard deviation)
Average data service to users
[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vhicles (UAVs): optimal transport theory for
hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
Research Trends for UAV-aided wireless communication
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UAV Resource Management
Optimal transport theory for hover time optimization[28]
Optimal hover time of UAV 𝑖 required to completely service the target area
• Control time which is not used for transmission(processing, computing, control signaling)
30
• 𝑁 : total # of users• 𝑢(𝑥, 𝑦) : load (in bits) of a user located at (𝑥, 𝑦)
• 𝐶𝑖𝐵𝑖 : Shannon capacity ( )
• 𝑔𝑖 : additional control time
[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vhicles (UAVs): optimal transport theory for
hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
effective data transmission time
Control time
Research Trends for UAV-aided wireless communication
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Research Trends for UAV-aided wireless communication
Performance Analysis
UAV with underlaid D2D communications[27]
Purpose
• Deployment of an UAV as a flying base station used to provide on the fly wireless communications to a given geographical area is analyzed(coverage and rate performance)
Assumption
• Downlink users located uniformly in the cell with density 𝜆𝑑𝑢(# of users per 𝑚2)
• D2D users whose distribution follows homogeneous Poisson Point Process 𝜱𝑩 with density 𝜆𝑑(# of pairs per 𝑚2)
• A D2D receiver connects to its corresponding D2D transmitter pair located at a fixed distance away
• Interference from the UAV and other D2D transmitters
31[29] M. Mozaffari et. al, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun.,
Feb. 2016
Interference
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Research Trends for UAV-aided wireless communication
Performance Analysis
UAV with underlaid D2D communications[27]
Impact of altitude on D2D coverage probability
• Coverage probability for D2D
• Coverage probability for Downlink user
32[29] M. Mozaffari et. al, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun.,
Feb. 2016
Optimal UAV altitude
Average coverage probability for D2D(no interference between the UAV and the D2D
transmitter)
Interference(D2D)
• 𝜆𝑑 : D2D density• 𝑑0 : D2D transmitter location• 𝑃𝑑 : D2D transmit power• 𝑃𝑢 : UAV transmit power• 𝑋𝑢 : UAV-D2D distance
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Performance Analysis
UAV with underlaid D2D communications[27]
Impact of altitude on D2D coverage probability
• Average rate
» Sum rate
33[29] M. Mozaffari et. al, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun.,
Feb. 2016
Assuming
Downlink user
D2D
Optimal UAV altitude
200m, 350m, 400m for
𝑑0 = 30m, 25m, 20m (D2D distance)
Research Trends for UAV-aided wireless communication
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Research Trends for UAV-aided wireless communication
Performance Analysis
BLoS range extension with OPAL using UAVs
Purpose
• To extend the range of a tactical military network using UAVs
» UAVs autonomously optimize the network connectivity by relocating themselves
Optimization objective
• Placing the UAV radio relay is to improve the capacity of the network
• Using Shannon-Hartley theorem
» Derived network quality(Network Connection Level)
» The higher the SNR, the higher the capacity
34[30] K. P. Hui, D. Phillips, A. kekirigoda, “Beyond line-of-sight range extension with OPAL using autonomous unmanned aerial vehicles,” in proc. MILCOM
2017, Baltimore, MD, USA, Oct, 2017
Network connection Level
• 𝑖 : UAV flight path• 𝑇𝑘 : Time
• 𝑉 : Set of nodes
• 𝐸 : directed edges connecting two nodes(measuring its link quality as a SNR)
※ OPAL : self-healing communications network concept (autonomous system)
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Research Trends for UAV-aided wireless communication
Performance Analysis
BLoS range extension with OPAL using UAVs
Scenario 1
• Two mobile ground node(node 1, node 2), UAV node
35
※ OPAL : self-healing communications network concept (autonomous system)
Node 1
Node 2
UAV
[30] K. P. Hui, D. Phillips, A. kekirigoda, “Beyond line-of-sight range extension with OPAL using autonomous unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017
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Research Trends for UAV-aided wireless communication
Performance Analysis
BLoS range extension with OPAL using UAVs
Scenario 2
• Base Station(node 1) Mobile ground node(node 2), UAV node
36
※ OPAL : self-healing communications network concept (autonomous system)
Node 1
Node 2
UAV
[30] K. P. Hui, D. Phillips, A. kekirigoda, “Beyond line-of-sight range extension with OPAL using autonomous unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017
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Research Interest
37
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Research Interest
UAV Wireless communication & Tactical Network
UAV 관련 MAC 프로토콜 개발 연구
TDMA 기반, 자가학습 관련 연구
차세대 대용량 다중접속 기술 연구(FNT-24)
주파수 효율 극대화 기법 및 대용량 변복조 기술을 통한 차세대 군 통합망 요소 기술 개발
38
• UAV 웨이브폼 기술연구
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Conclusion
39
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Conclusion
SummaryBackground and current status about UAV Definition of UAV
UAV application and market growth
Introduction to UAV-aided wireless communication Functions of UAV
• UAV-UAV, relay node, gateway
Advantages of UAV-aided wireless communication
Case of UAV-aided Wireless Communication(Commercial, Military)• Mobile base station, 5G communication, Integrated Network, UAV swarm, etc.
Design challenges of UAV-aided wireless communication Ensuring reliable network connectivity
Effective interference management techniques
Energy-aware UAV deployment and operation mechanism
Effective resource management and security mechanism
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Conclusion
Summary
Research Trends for UAV-aided wireless communication
A2G Channel Model
• LoS probability
• Shadowing model for HAP in built-up areas
• Rician fading model with
UAV deployment
• Next generation tactical communication networks with space and aerial
• Deployment strategies of multiple UAVs for optimal wireless coverage
» to maximize the downlink coverage performance
UAV Resource Management
• Bandwidth requirement
• Optimal transport theory for hover time optimization
Performance analysis
• Impact of altitude on coverage probability
• BLoS range extension with OPAL using UAVs 41
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Reference
42
[1] Joint Publication 1-02, “DOD Dictionary of Military and Associated Terms.”[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017,
Baltimore, MD, USA, Oct, 2017[3] Airbus, “Zephyr, High Altitude Pseudo-Satellite”[4] Google, “Loon Project”, https://x.company/projects/loon/[5] HIS Markit, http://news.ihsmarkit.com/press-release/aerospace-defense-security/significant-global-demand-pushes-uav-sales-exceed-
82-billio[6] I. Jawahr, N. Mohamed, J. A. Jaroodi, D. P. Agrawal, S. Zhang, “Communication and networking of UAV-based Systems: Classification
and associated architectures,” Journal of Network and Computer Application, vol. 84, pp. 93-108, Apr. 2017[7] KT, https://corp.kt.com/html/biz/services/trial.html [8] AT&T, “When COWs Fly: AT&T sending LTE Signals from drone,” http://about.att.com/innovationblog/cows_fly[9] Verizon, http://www.verizon.com/about/news/first-responders-make-calls-and-send-text-messages-using-flying-cell-site[10] SKT, http://www.sktelecom.co.kr/advertise/press_detail.do?idx=4190[11] Google, Project Skybender, https://www.theguardian.com/technology/2016/jan/29/project-skybender-google-drone-tests-internet-
spaceport-virgin-galactic[12] Intel, https://www.intel.com/content/www/us/en/drones/drone-applications/commercial-drones.html[13] China mobile, https://www.sdxcentral.com/articles/news/china-mobile-eyes-5g-enabled-drones-solve-network-latency/2016/08/[14] IBM Watson, https://www.ibm.com/watson[15] “Joint Concept for Command and Control of the Joint Aerial Layer Network”, Joint Chiefs of Staff, 2015.03[16] U.S Army, “Manned Unmanned Sytems Integration Capability: MUSIC”[17] ASIMUT, https://asimut.gforge.uni.lu/description.html[18] ‘Office of Naval Research’, https://www.onr.navy.mil[19] ‘U.S. Departure of Defense,’ https://www.defense.gov
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Reference
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[20] Y. Zeng, R. Zhang, T. J. Lim, “Wireless Communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communication Magazine, vol. 54, no. 5, pp. 36 – 42, May. 2016.
[21] A. A. Hourani, S. kandeepan, A. Jamalipour, “Modeling Air-to-Ground path loss for low altitude platforms in urban environments,” in proc. Globecom 2014, Austin, TX, USA, Dec. 2014.
[22] A. A. Hourani, S. Kandeepan, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Letters, vol. 3, no. 6, pp 569 – 572, Dec. 2014
[23] J. Holis, P. Pechac, “Elevation dependent shadowing model for mobile communications via high altitude platforms in built-up areas,” IEEE Trans. Antennas and propagation, vol. 56, no. 4, pp. 1078 – 1084, Apr. 2008.
[24] D. W. Matolak, R. Sun, “Air-Ground channel characterization for unmanned aircraft systems: the near-urban environment,” in Proc. MILCOM 2015, Tempa, FL, USA, Oct. 2015.
[25] 조준우, 오지훈, 이재문, 김동현, 김재현, “우주/공중 기반 기동통신망 핵심기술, “한국통신학회지(정보와 통신), 제 33권 11호, pp. 65 – 72, 2016년 11월
[26] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEE Communications Letters, vol. 20, no. 8, pp. 1647-1650, Aug. 2016.
[27] H. R. Cheon, J. W. Cho, J. H. Kim, “Dynamic resource allocation algorithm of UAS by network environment and data requirement,” in proc. ICTC 2017, jeju, Korea, 18 - 20, Oct. 2017.
[28] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Commuinication using unmanned aerial vehicles (UAVs): optimal transport theory for hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017
[29] M. Mozaffari et. al, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun., Feb. 2016
[30] K. P. Hui, D. Phillips, A. kekirigoda, “Beyond line-of-sight range extension with OPAL using autonomous unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017
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Thank you !
Q & A
44