5g & beyond

5G & Beyond: Embracing A New Era of Internet-of-Drones (IoD)

Rui Zhang (e-mail: [email protected])

Keynote SpeechIEEE SECON 2018 Workshop on CPC-UAV

11 June 2018, Hong Kong

SECON 2018 Workshop CPC-UAV 1

mailto:[email protected]

Outline

Rui Zhang, National University of Singapore

Part 1: UAV Meets Cellular − Two Paradigms Cellular-Connected UAV UAV-Assisted Terrestrial Communication

Part 2: UAV Communication Basics Channel Model Energy Model Trajectory Design

Part 3: Recent Results


Rui Zhang, National University of SingaporeIntroduction

UAV: Whose Time is Coming

Create more than 100,000 new jobs in US over the next 10 years Numerous applications: military, traffic control, cargo delivery, precision

agriculture, video streaming, aerial inspection, rescue and search, ……

Source: SDI analysis. https://www.marketresearch.com/product/sample-8691316.pdf



Aerial photography


Drone Delivery

Aerial Inspection Precision Agriculture


Traffic Monitoring

H. Menouar, et. al, “UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges”, IEEE Commun. Mag., Mar. 2017



Airborne Communication Platform

Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Commun. Mag., May 2016



FAA Rules for Small UAS June 2016: US Federal Aviation Administration (FAA) released new

rules for commercial use of small unmanned aircraft systems (UAS) (Part 107) Small UAS: 55 pounds (25 kg) Visual line-of-sight (VLoS) only Daylight-only operation Maximum ground speed: 100mph (161km/h) Maximum altitude: 400 feet (122 m) Operations are allowed in uncontrolled airspace (Class G) without air

traffic control (ATC) permission The road ahead:

Beyond VLoS operation? Completely autonomous flying?


Source: FAA, “Operation and Certification of Small Unmanned Aircraft Systems”, https://www.faa.gov/uas/media/RIN_2120-AJ60_Clean_Signed.pdf


Wireless Communications for UAVs: Basic Requirement Control and Non-Payload

Communications (CNPC) Ensure safe, reliable, and effective

flight operation Low data rate, high reliability, high

security, low latency Telemetry (UAV status reporting) Command and control Navigation aids Sense and avoid (S&A) Air traffic control (ATC) relay,……

Payload Communications Application specific information Typically higher rate than CNPC CNPC information flows [ITUReportM.2171]

Control Station UAV


ITU, “Characteristics of unmanned aircraft systems and spectrum requirements to support their safe operation in nonsegregated airspace,” Tech. Rep. M.2171, DEC., 2009.


Wireless Communication for UAVs: Existing Technologies Direct ground-to-UAV communications Unlicensed spectrum (2.4 GHz) Main limitations:

Unreliable Insecure Vulnerable to interference Limited data rate Visual line of sight (LoS) operation Difficult to legitimately monitor and manage


Rui Zhang, National University of SingaporeIntegrating UAVs into 5G and Beyond

Integrating UAVs into Cellular


Y. Zeng, J. Lyu, and R. Zhang, “Cellular-connected UAV: potentials, challenges and promising technologies,” submitted to IEEE Wireless Communications.


Integrating UAVs into Cellular: A Win-Win Technology

Integrating UAVs into cellular networks (LTE, 5G, B5G…) For UAV industry:

More reliable and secure CNPC Unlimited operation range without relying on satellite High capacity enabled by advanced cellular techniques Cost effective: avoid building new infrastructure for UAV

communication Enhanced air traffic management: legitimate control takeover when

needed For cellular industry:

New business opportunities by incorporating aerial users More robust and cost-effective cellular network with aerial

communication platform



Integrating UAVs into Cellular: Two Paradigms

Cellular-Connected UAV: UAVs as aerial users with their own missions

UAV-Assisted Terrestrial Communication: UAVs as aerial communication platforms (e.g. BS, relay, access point)

Cellular-Connected UAV UAV-Assisted Terrestrial Communication



UAV Communications: What’s New?


High altitude Up to 300 meters (as user in 3GPP) or even higher

High 3D mobility Partially or fully controllable trajectory

New air-to-ground channel characteristic High probability of line-of-sight (LoS)

Size, weight and power constraint (SWAP) Limited UAV on-board energy/endurance

New opportunities and challenges for both paradigms

Rui Zhang, National University of SingaporeCellular-Connected UAV

Cellular-Connected UAV High altitude

3D coverage is challenging: existing BS antenna tilted downwards High 3D mobility

Partially controllable trajectory due to mission requirement (e.g., cargo delivery) Asymmetric downlink/uplink: ultra-reliable CNPC versus high-rate payload data Strong air-to-ground LoS channel

Pro: High degrees of macro-diversity (beneficial for BS association, CoMP) Con: Severe aerial-ground interference (in both uplink and downlink)

Only served by antenna side-lobe or reflected signals with current LTE BS



Severe Aerial-Ground Interference

Strong LoS Interference between UAV and terrestrial users Uplink: more interference imposed to neighbouring BSs Downlink: more interference received from neighbouring BSs



Reshaping Cellular Networks to the Sky New techniques needed to enable highly heterogonous network with both

terrestrial and aerial users Possible spectrum allocation:

Protected aviation spectrum licensed to cellular operators for UAV CNPC Cellular spectrum shared by aerial and terrestrial users for payload data

Interference management techniques: Sub-sector in elevation domain 3D beamforming Multi-cell cooperation Terrestrial-aerial NOMA Joint trajectory design and communication resource allocation




Cellular-Connected UAV: Simulation Results (1/6)


Cellular-connected UAV in 3GPP urban macro (UMa) scenario 19 sites, each constituting 3 sectors (57 cells) Inter-site distance (ISD) 500 m, cell radius 166.7 m Two different BS array configurations: fixed pattern versus 3D beamforming





Cellular-connected UAV simulation setup:




Scenario 1: UAV communication in dedicated channel Focus on one particular UAV with horizontal coordinate (250 m, 100 m) One channel is allocated exclusively (interference-free) Three UAV altitudes: 1.5 m, 90 m, and 200 m

Scenario 2: Shared channel by UAV and ground user Multi-user downlink communication with both UAV and ground users All users share the same channel Total users is 20, but with varying number of aerial users (UAVs) Horizontal locations uniformly distributed Ground users: height fixed to 1.5 m Aerial users: altitude uniform between 1.5 m and 300 m




Fixed BS pattern: Low altitude: UAV likely to be associated with nearby cells (cells 1, 5, 9) High altitude: UAV likely to be associated with distant cells (via antenna side lobe)

3D beamforming: UAV associated with nearby cells for all altitudes




High altitude: less SNR variation (due to reduced scattering) 3D beamforming significantly improves SNR over fixed beam




Sum rate for different number of UAVs with shared channel Overall spectrum efficiency degrades as number of UAVs increases

Expected since aerial users suffer from more severe interference due to LoS-dominated channel


Cellular-Connected UAV: Industry Efforts

February 2016 Intel and AT&T Demonstrated the world’s first LTE-connected drone at the 2016 Mobile World Congress

August 2016 Ericsson and China Mobile

Claimed the world’s first 5G-enabled drone prototype field trial in WuXi of China

September 2016 Qualcomm Feasibility proof of drone operation over commercial LTE networks up to 400 feet

March 2017 3GPP Study item on “study on enhanced support for aerial vehicles using LTE”

May 2017 Qualcomm Trial report “LTE Unmanned Aircraft Systems”

November 2017 Huawei Wireless X Labs

Announced the "Digital Sky Initiative“ to “spur development of drone applications and enable the low airspace digitized economy via enhanced low airspace network coverage”

December 2017 3GPP 3GPP TR 36.777, Technical Report (Release 15)


Advantages of UAV-assisted communication On demand deployment, fast response Cost effective Fully controllable mobility in 3D Short-distance LoS link

Three typical use cases UAV-aided ubiquitous coverage UAV-aided mobile relaying UAV-aided information dissemination/data collection


UAV-Assisted Terrestrial CommunicationUAV-Assisted Terrestrial Communication


UAV-Aided Ubiquitous Coverage Provide seamless coverage within the serving area Application scenarios: fast service recovery after natural disaster BS offloading at hotspot

Rui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication

Y. Zeng, R. Zhang, and T. J. Lim, "Wireless communications with unmanned aerial vehicles: opportunities and challenges," IEEE Commun. Mag., May 2016


UAV-Aided Mobile Relaying Connecting two or more distant users or user groups Application scenarios: Emergency response, e.g., between frontline and headquarter Military network




UAV-Aided Information Dissemination/Data Collection Application scenarios:

Periodic sensing IoT communications Multicasting





UAV-mounted Aerial BS: Two Practical Realizations Tethered UAVs: UAVs connected with ground platform by cables (for power and/or

data backhaul) Examples: AT&T’s “flying cell-on-wings (COWs)”, Facebook’s “Tether-Tenna”, EE’s “Air

Masts” Untethered UAVs: Wireless, free-fly drones Examples: Nokia’s flying-cell (F-Cell) and Facebook’s Aquila

AT&T’s flying COW Nokia’s F-Cell


Cable

Source: http://www.zdnet.com/article/at-t-completes-its-first-flying-cow-test-flight/https://www.fiercewireless.com/tech/nokia-bell-labs-uses-drone-to-deliver-breakthrough-f-cell-to-office-rooftop

UAV-Assisted Terrestrial Communication

http://www.zdnet.com/article/at-t-completes-its-first-flying-cow-test-flight/

https://www.fiercewireless.com/tech/nokia-bell-labs-uses-drone-to-deliver-breakthrough-f-cell-to-office-rooftop

Outline






UAV-BS Channel ModelRui Zhang, National University of Singapore

Free-space path loss model: high altitude or rural environment

Height-dependent channel parameters: path loss exponent and shadowing variance decrease with altitude

Depression-angle dependent model: additional term depends on depression angle (positive or negative)

Matolak’s model: modified log-distance path loss model with a correction for flight direction, wideband

3GPP channel model [3GPP TR 36.777]: depends on the BS and UAV height; three scenarios: RMa (Rural Macro), UMa (Urban Macro), UMi(Urban Micro)


UAV Channel Model

UAV-Terminal Channel ModelRui Zhang, National University of Singapore

Free-space path loss model Rician fading model: Rician factor 15 dB for L-band and 28 dB for C-band

in hilly terrain Probabilistic LoS model: LoS and NLoS components, excessive path loss

due to NLoS modelled as Gaussian Elevation-angle dependent model: LoS probability increases with

elevation angle 𝜃𝜃 (urban environment)


UAV Channel Model

UAV Energy Consumption ModelRui Zhang, National University of SingaporeUAV Energy Model

Limited on-board energy: critical issue in UAV communication, for both UAV as user or BS/relay

UAV energy consumption: Propulsion energy >> Communication energy Empirical and Heuristic Models:

Empirical model based on measurement result Fuel cost modelled by L1 norm of control force Fuel cost proportional to the square of speed

Analytical Model Closed-form model based on well-established results in aircraft literature Power as a function of speed and acceleration: fixed-wing or rotary-wing

32SECON 2018 Workshop CPC-UAV

UAV Energy Model (Fixed-Wing)Rui Zhang, National University of Singapore

Forces on UAV: weight (W), lift (L), drag (D), and thrust (F) Fixed-wing propulsion energy (Straight and level flight)

Convex function: first decreasing then increasing with speed


Y. Zeng and R. Zhang, "Energy-Efficient UAV Communication with Trajectory Optimization," IEEE Trans. Wireless Commun., June 2017.

UAV Energy Model

UAV Energy Model (Rotary-Wing)Rui Zhang, National University of Singapore

Rotary-wing propulsion power

Non-convex function: first decreasing then increasing with speed Maximum-endurance (me) speed Maximum-range (mr) speed


Energy per unit distance

Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wingUAV,” submitted to IEEE Trans. Wireless Commun.

UAV Energy Model

UAV Trajectory DesignRui Zhang, National University of SingaporeUAV Trajectory Design

UAV Trajectory: A new design DoF for communication performance enhancement

Cellular-connected UAV: UAV has its own mission: partial trajectory adaptation E.g., avoid coverage hole, aerial highway, interference avoidance

UAV-assisted communication: UAV dedicated for serving ground users More freedom on trajectory design for enhanced communication performance

Common constraints on trajectory design: Initial/final status (location, velocity, acceleration) Maximum/minimum UAV speed Maximum acceleration or turning angle Collision avoidance No-flying zone …


UAV Trajectory Optimization: Useful TechniquesRui Zhang, National University of Singapore

Graph theory and shortest path problem Travelling salesman problem (TSP) and TSP with neighborhood (TSPN) Pickup-and-delivery problem (PDP) Mixed integer linear programming (MILP) Block coordinate decent/ADMM (for iterative/parallel trajectory and

communication resource allocation optimization) Successive convex approximation

36

Global concave rate lower bound

SECON 2018 Workshop CPC-UAV

Y. Zeng, R. Zhang, and T. J. Lim, “Throughput maximization for UAV-enabled mobile relaying systems,” IEEE Trans. Commun., Dec. 2016.

UAV Trajectory Design


QoS-Aware Trajectory Design for Cellular-Connected UAV Each BS has certain coverage region for aerial users Optimize the UAV trajectory to

Minimize travelling time from initial to final locations Ensure connectivity constraint at all time

Near-optimal solution with graph theory and convex optimization

S. Zhang, Y. Zeng, R. Zhang, “Cellular-Enabled UAV Communication: Trajectory Optimization Under Connectivity Constraint “, IEEE ICC, May 2018.



Connectivity-Constrained UAV Trajectory Optimization



Throughput Maximization for UAV RelayingUAV-Enabled Mobile Relaying


UAV-enabled relay moves at a constant altitude H Relay mobility constraints: (i) Maximum speed; (ii) Initial and final location S-R and R-D channels vary with the relay location (x(t),y(t)) Adaptive rate/power transmission by source and relay based on the

predictable channels Problem: maximize end-to-end throughput via joint power/rate allocation

and trajectory design



Problem FormulationUAV-Enabled Mobile Relaying

aggregate rate at destination

information-causality constraint

power constraint

initial location constraint

speed constraint

final location constraint

Communication Constraints

Mobility Constraints

Fundamental Tradeoffs in UAV Trajectory and Communication Design




Throughput-Delay Tradeoff Throughput-Energy Tradeoff Delay-Energy Tradeoff

Q. Wu, L. Liu, and R. Zhang, “Fundamental tradeoffs in communication and trajectory design for UAV-enabled wireless network,” submitted to IEEE Wireless Communications.

Throughput-Delay Tradeoff Rui Zhang, National University of Singapore



Throughput-Energy Tradeoff Rui Zhang, National University of Singapore



Outline






Field measurement over LTE network: Qualcom, Huawei, … Overview

• B. V. D. Bergh, A. Chiumento, and S. Pollin, “LTE in the sky: trading off propagation benefits with interference costs for aerial nodes,” IEEE Commun. Mag., May 2016.

• X. Lin, et al., “The sky is not the limit: LTE for unmanned aerial vehicles,” IEEE Commun. Mag., 2018.• Y. Zeng, J. Lyu, and R. Zhang, "Cellular-connected UAV: potentials, challenges and promising technologies," submitted to IEEE

Wireless Commun.

UAV-BS Channel measurement and modeling• Rafhael Amorim, et al., “Radio channel modeling for UAV communication over cellular networks”, IEEE Wireless Commun. Lett., Aug.,

2017.• D. W. Matolak and R. Sun, “ Air–Ground Channel Characterization for Unmanned Aircraft Systems—Part I: Methods, Measurements,

and Models for Over-Water Settings,” IEEE Trans. Veh. Techn., Jan. 2017• A. Al-Hourani and K. Gomez, "Modeling cellular-to-UAV path-loss for suburban environments," IEEE Wireless Commun. Lett., Feb. 2018.

Performance analysis and simulation• M. M. Azari, Y. Murillo, O. Amin, F. Rosas, M. S. Alouini, and S. Pollin, “Coexistence of Terrestrial and Aerial Users in Cellular Networks,”

IEEE Globecom 2017 Workshop• M. M. Azari, F. Rosas, and S. Pollin, “Reshaping Cellular Networks for the Sky: The Major Factors and Feasibility,” arXiv• H. C. Nguyen, R. Amorim, J. Wigard, I. Z. Kovcs, T. B. Srensen, and P. Mogensen, “How to ensure reliable connectivity for aerial vehicles

over cellular networks,” IEEE Access, Feb. 2018.

Massive MIMO for drones• P. Chandhar, D. Danev, and E. G. Larsson, “Massive MIMO for communications with drone swarms,” IEEE Trans. Wireless Commun., Mar.

2018.• G. Geraci, A. G. Rodriguez, L. G. Giordano, D. L. Perez, and E. Bjornson, “Understanding UAV cellular communications: From existing

networks to massive MIMO,” Arxiv

Millimeter wave UAV• Z. Xiao, P. Xia, and X.-G. Xia, “Enabling UAV Cellular with Millimeter-Wave Communication: Potentials and Approaches, IEEE Commun.

Mag., May 2016.

Trajectory design with connectivity constraint• S. Zhang, Y. Zeng, and R. Zhang, “Cellular-enabled UAV communication: Trajectory optimization under connectivity constraint ,” ICC

2018 • E. Bulut and I. Guvenc, “Trajectory Optimization for Cellular-Connected UAVs with Disconnectivity Constraint,” ICC 2018 Workshop


Cellular-Connected UAV: Recent ResultsCellular-Connected UAV


Overview• Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Commun.

Mag., May 2016• I. B. Yaliniz and H. Yanikomeroglu, “The new frontier in RAN heterogeneity: multi-tier drone-cells,” IEEE Commun. Mag., Nov. 2016 • Q. Wu, L. Liu, and R. Zhang, “Fundamental tradeoffs in communication and trajectory design for UAV-enabled wireless network,” IEEE

Wireless Communications, submitted

UAV-ground channel measurement and modeling• A. A. Hourani, S. Kandeepan, and A. Jamalipour, “Modeling air-to-ground path loss for low altitude platforms in urban environments,” in Proc.

IEEE Global Commun. Conf. (Globecom), 2014.• D. W. Matolak and R. Sun, “Unmanned aircraft systems: air-ground channel characterization for future applications,” IEEE Veh. Technol. Mag.,

Jun. 2015. • W. Khawaja, I. Guvenc, D. W. Matolaky, U. C. Fiebigz, and N. Schneckenberger, “A survey of air-to-ground propagation channel modelling for

unmanned aerial vehicles,” arXiv• L. Zeng, X. Cheng, C.-X. Wang, and X. Yin, “A 3D geometry-based stochastic channel model for UAV-MIMO channels,” in Proc. IEEE Wireless

Commun. Net. Conf. (WCNC), Mar. 2017.

UAV as quasi-stationary BS/relay 2D/3D BS placement

• A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP Altitude for Maximum Coverage,” IEEE Wireless Commun. Lett., vol. 3, no. 6, Dec. 2014, pp. 569–72.

• R. Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Efficient 3-D placement of an aerial base station in next generation cellular networks,” in ICC, Kuala Lumpur, Malaysia, May 2016

• Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEE Commun. Letters, Aug. 2016.

• E. Kalantari, H. Yanikomeroglu, and A. Yongacoglu, “On the number and 3D placement of drone base stations in wireless cellular networks,” in VTC-Fall, Sep. 18-21, 2016.

• M. Alzenad, A. El-keyi, F. Lagum, and H. Yanikomeroglu, “3D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage,” IEEE Wireless Commun. Lett., Aug. 2017.

• J. Lyu, Y. Zeng, R. Zhang, and T. J. Lim, “Placement optimization of UAV-mounted mobile base stations,” IEEE Commun. Letters, vol. 21, no. 3, pp. 604–607, Mar. 2017.


UAV-Assisted Communication: Recent Results (1/5)UAV-Assisted Terrestrial Communication


UAV as quasi-stationary BS/relay/AP Optimal altitude with adjustable beamwidth

• H. He, S. Zhang, Y. Zeng, and R. Zhang, "Joint altitude and beamwidth optimization for UAV-enabled multiuser communications," IEEE Commun. Lett., Feb., 2018

Performance analysis• M. Mozaffari et al., “Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis,” Proc. IEEE GLOBECOM, San

Diego, CA, Dec. 2015• M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlaid device-to-device communications:

Performance and tradeoffs,” IEEE Trans. Wireless Commun., Jun. 2016.• C. Zhang and W. Zhang, “Spectrum Sharing for Drone Networks,” IEEE JSAC, 2016• A. Al-Hourani, S. Chandrasekharan, G. Kaandorp, W. Glenn, A. Jamalipour, and S. Kandeepan, “Coverage and rate analysis

of aerial base stations,” IEEE Trans. Aerospace and Elect. Sys., Dec. 2016. • V. V. Chetlur and H. S. Dhillon, “Downlink coverage analysis for a finite 3-D wireless network of unmanned aerial

vehicles,” IEEE Trans. Commun., Oct. 2017 • M. M. Azari, F. Rosas, K.-C. Chen, and S. Pollin, “Ultra reliable UAV communication using altitude and cooperation

diversity,” IEEE Trans. Commun., Jan. 2018

Radio map • J. Chen and D. Gesbert, “Optimal positioning of flying relays for wireless networks: A LOS map approach,” in Proc. IEEE Int. Conf.

Commun., Paris, France, May 2017.• J. Chen, U. Yatnalli, and D. Gesbert, “Learning radio maps for UAV aided wireless networks: A segmented regression approach,” in

Proc. IEEE Int. Conf. Commun., Paris, France, May 2017.• S. Bi, J. Lyu, Z. Ding, and R. Zhang, “Engineering radio map for wireless resource management,” submitted to IEEE Wireless

Communications




UAV as mobile BS/relay/AP Trajectory optimization

• Z. Han, A. L. Swindlehurst, and K. J. R. Liu, “Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles,” IEEE Trans. Veh. Technol., Sep. 2009

• F. Jiang and A. L. Swindlehurst, “Optimization of UAV heading for the ground-to-air uplink,” IEEE J. Sel. Areas Commun., Jun. 2012.• Y. Zeng, R. Zhang, and T. J. Lim, "Throughput maximization for UAV-enabled mobile relaying systems," IEEE Trans. Commun.,

Dec., 2016• Y. Zeng, X. Xu, and R. Zhang, "Trajectory optimization for completion time minimization in UAV-enabled multicasting,“ IEEE

Trans. Wireless Commun., April 2018• C. Zhan, Y. Zeng, and R. Zhang, “Trajectory design for distributed estimation in UAV enabled wireless sensor network,” IEEE

Transactions on Vehicular Technology, submitted.

Joint resource allocation and trajectory optimization• Q. Wu, Y. Zeng, and R. Zhang, "Joint trajectory and communication design for multi-UAV enabled wireless networks," IEEE

Trans. Wireless Commun., March 2018• Q. Wu, J. Xu, and R. Zhang, “Capacity characterization of UAV-enabled two-user broadcast channel,” IEEE Journal on Selected

Areas in Communications, accepted. • J. Zhang, Y. Zeng, and R. Zhang, “UAV-enabled radio access network: multi-mode communication and trajectory design,” IEEE

Transactions on Signal Processing, submitted

Throughput-delay trade-off• J. Lyu, Y. Zeng, and R. Zhang, "Cyclical multiple access in UAV-aided communications: a throughput-delay tradeoff'', IEEE

Wireless Commun. Lett., Dec., 2016• Q. Wu and R. Zhang, “Common throughput maximization in UAV-enabled OFDMA system with delay consideration,” IEEE Trans.

Commun., submitted




UAV as mobile BS/relay/AP Hybrid UAV and ground BSs

• J. Lyu, Y. Zeng, and R. Zhang, “UAV-aided offloading for cellular hotspot,” IEEE Trans. Wireless Commun, accepted.

UAV meets caching• M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, “Caching in the sky: proactive deployment of cache-enabled

unmanned aerial vehicles for optimized quality-of-experience,” IEEE J. Sel. Areas Commun., May 2017.• X. Xu, Y. Zeng, Y. L. Guan, and R. Zhang, “Overcoming endurance issue: UAV-enabled communications with proactive caching,”

IEEE J. Sel. Areas Commun., accepted

UAV Enabled Wireless Power Transfer/WPCN• J. Xu, Y. Zeng, and R. Zhang, “UAV-enabled wireless power transfer: trajectory design and energy optimization,” IEEE Trans.

Wireless. Commun., accepted • L. Xie, J. Xu, and R. Zhang “Throughput maximization for UAV-enabled wireless powered communication networks,” IEEE VTC

2018

Secure UAV Communication• A. Li, Q. Wu, and R. Zhang, “UAV-enabled cooperative jamming for improving secrecy of ground wiretap channel,” IEEE Wireless

Commun. Lett., submitted• G. Zhang, Q. Wu, M. Cui, and R. Zhang, “Securing UAV communications via joint trajectory and power control,” IEEE Trans.

Wireless Commun., submitted • G. Zhang, Q. Wu, M. Cui, and R. Zhang, “Securing UAV communications via trajectory optimization,” IEEE Globecom 2017

UAV-based CoMP• L. Liu, S. Zhang, and R. Zhang, “CoMP in the sky: UAV placement and movement optimization for multi-user

communications,” IEEE Trans. Wireless Commun., submitted




Energy-efficient UAV communications UAV propulsion energy

• Y. Zeng and R. Zhang, "Energy-Efficient UAV Communication with Trajectory Optimization," IEEE Trans. Wireless Commun., Jun., 2017.

• Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Trans. Wireless Commun., submitted

• J. Zhang, Y. Zeng, and R. Zhang, “Spectrum and energy efficiency maximization in UAV-enabled mobile relaying,” IEEE International Conference on Communications (ICC), 2017.

UAV transmission energy• M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile internet of things: Can UAVs provide an energy-efficient

mobile architecture,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2016

Energy minimization of ground nodes• C. Zhan, Y. Zeng, and R. Zhang, "Energy-efficient data collection in UAV enabled wireless sensor network,“ IEEE

Wireless Commun. Lett., accepted.

Energy trade-off between UAV and ground nodes• D. Yang, Q, Wu, Y. Zeng, and R. Zhang, “Energy trade-off in ground-to-UAV communication via trajectory design,” IEEE

Trans. Veh. Technol., accepted.

Energy-efficient UAV edge computing• S. Jeong, O. Simeone, and J. Kang, “Mobile edge computing via a UAV- mounted cloudlet: Optimization of bit allocation

and path planning,” IEEE Trans. Veh. Technol., 2017.




Spiral-Based Aerial BS Placement: Fixed AltitudeRui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


Minimize required number of UAVs to ensure all terminals are covered

Core-sets based algorithm, optimal, but with exponential complexity

Proposed spiral-based BS placement algorithm

Example with 80 terminals: Proposed spiral: 11 BSs Optimal core-sets: 11 BSs Benchmark strip-based: 13

BSs

J. Lyu, Y. Zeng, R. Zhang, and T. J. Lim, "Placement Optimization of UAV-Mounted Mobile Base Stations", IEEE Commun. Letters, vol. 21, no. 3, pp. 604-607, Mar. 2017.

Optimal UAV Altitude with Adjustable BeamwidthRui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


UAV antenna with adjustable beamwidth

Fly-hover-communicate protocol Downlink multicasting: altitude as high as

possible, optimal beamwidth decreases with altitude

Downlink broadcasting: altitude as low as possible, beamwidth as small as possible

Uplink multiple access: altitude/beamwidthdoesn’t matter

H. He, S. Zhang, Y. Zeng, and R. Zhang, "Joint altitude and beamwidth optimization for UAV-enabled multiuser communications," IEEE Commun. Lett., Feb. 2018.

UAV-Enabled MulticastingRui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


Y. Zeng, X. Xu, and R. Zhang, "Trajectory design for completion time minimization in UAV-enabled multicasting“, IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2233-2246, April 2018.

UAV-enabled multicasting with random linear network coding: common information for multiple ground nodes

Applications: public safety communication, emergency response, video streaming, intelligent transportation systems

Minimize mission completion time, while satisfying target file recovery probability (each ground node needs to have a minimum “contact” duration with the UAV)

Optimal trajectory design is challenging (NP-hard): virtual BS placement for waypoint design travelling salesman for visiting order linear programming for optimal speed

UAV-Enabled Multicasting: Optimized TrajectoryRui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


Benchmark: strip-based trajectoryTotal traveling distance: 13.9 kmRequired time: 279 s

Optimized trajectoryTotal traveling distance: 8.1 kmRequired time: 173.5 s

Y. Zeng, X. Xu, and R. Zhang, "Trajectory design for completion time minimization in UAV-enabled multicasting“, IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2233-2246, April 2018.

UAV Communications with Caching



Critical issue in UAV communication: limited UAV endurance (e.g., ~30mins) Proposed solution: exploiting ground user caching Phase 1: UAV caches files to the selected ground users Phase 2: D2D file sharing among ground users Fundamental trade-off in file caching time and file retrieval time: joint trajectory

and caching design

X. Xu, Y. Zeng, Y. L. Guan, and R. Zhang, “Overcoming endurance issue: UAV-enabled communications with proactive caching,” IEEE JSAC, accepted

UAV Communications with Caching



Multi-UAV Enabled Networks with Coordination Rui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication

57

Multiuser downlink communication: broadcast channel (BC) + interference channel (IFC)

New Interference-mitigation approach: coordinated trajectory design

Q. Wu, Y. Zeng, and R. Zhang, “Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks,” IEEE Trans. Wireless Commun., Mar. 2018

SECON 2018 Workshop CPC-UAV

Secure UAV-Ground CommunicationsRui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


Physical layer security via joint transmit power control and trajectory design

G. Zhang, Q. Wu, M. Cui, and R. Zhang, “Securing UAV communications via trajectory optimization,” IEEE Globecom, Dec. 2017.

UAV-assisted Jamming Rui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


Opportunistic UAV jamming to assist in secure terrestrial communication

A. Li, Q. Wu, and R. Zhang, “UAV-enabled cooperative jamming for improving secrecy of ground wiretap channel,” submitted to IEEE Wireless Communications Letters.

UAV-Enabled Wireless Power Transfer (WPT)Rui Zhang, National University of SingaporeUAV-Assisted Terrestrial Communication


J. Xu, Y. Zeng, and R. Zhang, “UAV-enabled wireless power transfer: Trajectory design and energy optimization,” IEEE Trans. Wireless Commun., accepted.

A UAV-mounted mobile energy transmitter (ET) is dispatched to deliver wireless energy to a set of on-ground energy receivers (ERs) Facilitate large-scale implementation of

future WPT systems with improved energy transfer efficiency

Optimize UAV trajectory to maximize the minimum received energy among all ERs

Main techniques: Multi-location hovering by ignoring the

maximum UAV speed constraints Successive hover-and-fly via travelling

salesman and successive convex programming (SCP)

-10 -8 -6 -4 -2 0 2 4 6 8 10

x(m)

-10

-8

-6

-4

-2

0

2

4

6

8

10

y(m

)

ER locations

Multi-location hovering

Successive hover-and-fly

SCP-based trajectory

ER 1 ER 2 ER 4

ER 6

ER 7

ER 9

ER 3

ER 5

ER 8

ER 10

ConclusionsRui Zhang, National University of Singapore


Integrating UAVs into 5G and beyond: a new and promising paradigm to embrace the forthcoming IoD (internet-of-drones) era

Cellular-Connected UAV: UAV as aerial user 3D aerial coverage Aerial-terrestrial interference management Communication-aware trajectory design

UAV-Assisted Terrestrial Communication: UAV as BS/relay 3D BS/relay placement, movement control Exploit UAV controlled trajectory for enhanced communication New throughput-delay, throughput-energy trade-offs

Much more to be done…


Cellular-Connected UAV: Promising Research Directions New architectures/protocols for cellular-connected UAVs Field measurement and prototype for LTE-connected UAVs Channel measurement and modelling for UAV-BS communications Performance analysis for 3D aerial coverage Security and safety issues of UAV systems Spectrum management and multiple access schemes Interference mitigation for cellular-connected UAVs Cellular systems with coexisting aerial and terrestrial users 3D beamforming MIMO/massive MIMO/mmWave technologies for cellular UAV systems Offline/online UAV trajectory design with QoS constraints Cellular-enabled UAV swarm ……



UAV-Assisted Communication: Promising Research Directions New protocols and use cases for aerial communication platform Channel measurement and modelling for UAV-terminal and UAV-UAV links Multi-UAV cooperative communication/interference management UAV-aided information dissemination/data collection/offloading UAV communication with limited buffer size/energy/wireless backhaul Throughput-delay trade-off in UAV communications Physical-layer security in UAV-ground communications Energy-efficient joint communication and trajectory optimization UAV-ground energy/throughput trade-off UAV BS/relay 3D placement and dynamic movement UAV and ground BS coexistence and spectrum sharing MIMO/massive MIMO/mmWave for UAV-assisted communications UAV meets wireless power/energy harvesting/caching/edge computing …..

UAV-Assisted Terrestrial Communication


5g & beyond

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