국방 NCW 포럼 특별세션

UAV 활용 무선통신 기술

2017. 11. 29

Jae-Hyun Kim

jkim@ajou.ac.kr

Wireless Internet aNd Network Engineering Research Lab.

http://winner.ajou.ac.kr

School of Electrical and Computer Engineering

Ajou University, Korea

Contents

Introduction

Research Trends for UAV-aided wireless communication

Research Interest

Conclusion

2

1

2

3

4

Introduction

3

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>

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)

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

[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>

[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)

Facebook

China Mobile(2016)

UAV-based IoT platform

Intel(2016)

Introduction

8

<Intel> <China mobile>

<Google><IBM>

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

[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

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

Research Trends for UAV-aided wireless communication

12

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

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

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

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

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.

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

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

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

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

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

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

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.

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.

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

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

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

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

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

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

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

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

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)

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

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

Research Interest

37

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

Conclusion

39

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

40

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

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

Reference

43

[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.

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Thank you !

Q & A

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