Accessing from the sky: UAV challenges from a ... · Google, Facebook, Amazon, Huawei , Qualcomm,...
Transcript of Accessing from the sky: UAV challenges from a ... · Google, Facebook, Amazon, Huawei , Qualcomm,...
Accessing from the sky: UAV challenges from a communication and signal processing perspective
Rui Zhang
IEEE Fellow/Distinguished LecturerClarivate Analytics Highly Cited Researcher
Dean’s Chair Assoc. Professor, National University of Singapore
(e-mail: [email protected])
IEEE SPAWC 2019 2-5 July 2019, Cannes, France
SPAWC 2019 1
Outline Introduction UAV communication requirement Wireless technologies for UAV The new paradigm: Integrating UAVs into cellular
What’s new over terrestrial communications?
UAV Communication Fundamentals
Challenges and Promising Solutions UAV-assisted wireless communication (UAV as BS/relay) Cellular-connected UAV (UAV as user/terminal)
Conclusions
Rui Zhang, National University of Singapore
SPAWC 2019 2
Rui Zhang, National University of SingaporeIntroduction
UAVs/Drones: Whose Time is Coming
SPAWC 2019 3
Anticipated global consumer UAV market by 2020 16 million UAVs with total market value over 120 billion USD
In June 2016, the U.S. Federal Aviation Administration (FAA) released the UAS operational rules Civilian use of small unmanned aircraft systems (UAS) with aircraft weight
less than 55 pounds (25 kg) In November 2017, FAA further launched “Drone Integration Pilot Program”
Expanded use of drones, including beyond-visual-line-of-sight (BVLoS) flights, night-time operations, flights over people, etc.
In March 2017, 3GPP launched new study item on “enhanced support for aerial vehicles using LTE” Special use cases also considered for 5G NR, V2X, etc.
Numerous pilot projects, prototypes, field measurements by industry giants Google, Facebook, Amazon, Huawei, Qualcomm, AT&T, Nokia, etc.
Rui Zhang, National University of SingaporeIntroduction
UAV Applications
SPAWC 2019 4
Aerial photography Drone DeliveryInspection Precision Agriculture
Traffic offloading Mobile relaying IoT Data Harvesting
Rui Zhang, National University of SingaporeIntroduction
Notre-Dame de Paris Fire Fight with Drones
SPAWC 2019 5
DJI Drones Assisted Notre Dame Fire-Fighters to Defeat Fire in Paris, France
https://www.droningon.co/2019/04/17/dji-drones-assist-notre-dame-fire-fighters-paris/
Rui Zhang, National University of Singapore
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
Payload Communications Application specific data (e.g.,
HD/4K video) Much higher rate than CNPC, less
stringent on reliability/latency
CNPC information flows [ITUReportM.2171]
UAVControl Station
UAV communication requirement
ITU, “Characteristics of unmanned aircraft systems and spectrum requirements to support their safe operation in non-segregated airspace,” Tech. Rep. M.2171, Dec. 2009.
SPAWC 2019 6
Rui Zhang, National University of Singapore
3GPP UAV Communication RequirementUAV communication requirement
Data Type Data Rate Reliability Latency
Downlink (DL: BS to UAV)
Command and control
60-100 Kbps
10−3 packet error rate
50 ms
Uplink (UL: UAV to BS)
Command and control
60-100 Kbps
10−3 packet error rate
--
Application data
Up to 50 Mbps
-- Similar toterrestrial user
3GPP TR 36.777: “Technical specification group radio access network: study on enhanced LTE support for aerial vehicles”, Dec. 2017.
SPAWC 2019 7
Rui Zhang, National University of Singapore
Existing Wireless Technologies for UAV Communications Wireless Technologies for UAV
Satellite-UAV communicationFlying ad-hoc network of UAVs
Direct UAV-ground communication in unlicensed spectrum (e.g. 2.4 GHz)
SPAWC 2019 8
Rui Zhang, National University of Singapore
New Paradigm: Cellular-Connected UAV Wireless Technologies for UAV
SPAWC 2019 9
Rui Zhang, National University of Singapore
Comparison of Wireless Technologies for UAV Wireless Technologies for UAV
SPAWC 2019 10
Technology Advantages Disadvantages
Direct link
• Simple• Low cost
• Limited range/data rate• Vulnerable to interference • Non-scalable for massive UAV
deployment
Satellite
• Global coverage • Costly • Heavy/bulky/energy
consuming equipment• High latency
Ad-hoc network• Robust and adaptable• Support for high
mobility
• Intermittent connectivity• Complex routing protocol• Low spectrum efficiency
Cellularnetwork
• Almost ubiquitousaccessibility
• Cost-effective• Superior performance
and scalability
• Unavailable in remote areas• Potential interference with
terrestrial communications
Rui Zhang, National University of Singapore
Future UAV Network: An Integrated ArchitectureWireless Technologies for UAV
Y. Zeng, Q. Wu, and R. Zhang, “Access from the Sky: a tutorial on UAV communications for 5G and beyond,’’ submitted to Proceedings of the IEEE, Invited Paper, available on arXiv
SPAWC 2019 11
Rui Zhang, National University of Singapore
New Paradigm: Integrating UAVs into Cellular Cellular-Connected UAV: UAV as new aerial user/UE in cellular network
UAV-Assisted Wireless Communication: UAV as new aerial platform (BS/AP, relay)
Integrating UAVs into 5G and Beyond
SPAWC 2019 12
Rui Zhang, National University of Singapore
Integrating UAVs into 5G: A Win-Win Technology
5G for UAVs: URLLC (with <20ms latency, >99.99% reliability): more secure CNPC eMBB (with 20 Gbps peak rate): real-time UHD video payload for VR/AR mMTC/D2D: UAV swarm communications and networking Cellular positioning (with cm accuracy): UAV localization/detection Massive MIMO: 3D coverage, aerial-terrestrial interference mitigation Edge-computing: UAV computing offloading, autonomous flight
UAVs for 5G: New business opportunities by incorporating new aerial users More robust and cost-effective cellular network with new aerial
communication platforms
Integrating UAVs into 5G and Beyond
SPAWC 2019 13
Rui Zhang, National University of Singapore
UAV Communications: What’s New over Terrestrial?Integrating UAVs into 5G and Beyond
Y. Zeng, Q. Wu, and R. Zhang, “Access from the Sky: a tutorial on UAV communications for 5G and beyond,’’ submitted to Proceedings of the IEEE, Invited Paper, available on arXiv
SPAWC 2019 14
Characteristic Opportunities Challenges
High altitude • Wide ground coverage as aerial BS/relay
• Require 3D cellular coverage foraerial user
High LoSprobability
• Strong and reliable communication link
• High macro-diversity• Slow communication scheduling
and resource allocation
• Severe aerial-terrestrial interference
• Susceptible to terrestrial jamming/eavesdropping
High 3D mobility
• Traffic-adaptive movement• QoS-aware trajectory design
• Handover management• Wireless backhaul
Size, weight, and power (SWAP) constraint
• Limited payload and endurance• Energy-efficient design, wireless
charging• Compact and lightweight
antenna/RF design
Outline
Introduction
UAV Communication Fundamentals Channel model UAV energy consumption model Performance metric Mathematical formulation
Challenges and Promising Solutions
Conclusions
Rui Zhang, National University of Singapore
SPAWC 2019 15
UAV Channel Modelling: An OverviewRui Zhang, National University of SingaporeUAV Channel Model
UAV-UAV UAV-GroundBS-UAV UAV-terminal
Large-scale: Pathloss + shadowing
Free-space path loss model
Customized models for UAV-ground links
Small-scale: multipath fading
Usually negligible
Rician, Nakagami-m
Rayleigh, Rician, Nakagami-m
Generic narrowband (frequency-flat) fading channel model
SPAWC 2019 16
Large-scale gain Small-scale fading
( )[ ] ( ) [ ] [ ]10 010 logd dB d X dB X dBσβ α− = + +
Free-Space Path Loss ModelRui Zhang, National University of Singapore
Channel power inversely proportional to distance square
No shadowing or small-scale fading Applicable scenarios: rural area, sufficiently high UAV altitude Pros:
Simple, channel highly predictable Useful for offline UAV trajectory design
Cons: Oversimplified in urban environment Fails to model change of propagation environment at different UAV
locations/altitude
UAV Channel Model
SPAWC 2019 17
Altitude/Height-Dependent Channel ParametersRui Zhang, National University of Singapore
Channel modelling parameters are functions of UAV altitude/height Altitude-dependent path loss exponent, shadowing variance, Rician
factor,…
Applicable scenarios: urban/suburban area, medium-to-high altitude Pros:
Captures environmental variations as altitude changes Useful for performance analysis, off-line trajectory design
Cons: Fails to model the change of propagation condition with horizontal fly
UAV Channel Model
R. Amorim et al., “Radio channel modeling for UAV communication over cellular networks,” IEEE Wireless Commun. Lett., Aug. 2017.
SPAWC 2019 18
( ) ( )( )1 2 10max log ,2U UH p p Hα = −
Elevation Angle-Dependent Channel ParametersRui Zhang, National University of Singapore
Elevation angle-dependent path loss exponent, Rician factor,… Applicable scenarios: urban/suburban area, low-medium altitude Pros:
Captures environmental variations with 3D flying Useful for performance analysis, off-line trajectory design
UAV Channel Model
Cons: Further experimental
verification is required
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.
SPAWC 2019 19
H1
d2D
LoS path
ϴ1 ϴ2
Reflected path
H2
Elevation Angle-Dependent Probabilistic LoS ModelRui Zhang, National University of Singapore
Separately model LoS and NLoS propagations LoS probability increases with elevation angle
Applicable scenarios: urban environment with statistical information of building height/distribution
Pros: Useful for performance analysis, off-line trajectory design Cons:
Model in statistical sense only, may fail in actual environment Needs experimental verification
UAV Channel Model
A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wireless Commun. Lett., Dec. 2014.
SPAWC 2019 20
( ) 0
0
, LoS enviroment , NLoS enviroment
dd
d
α
α
ββ
κβ
−
−
=
( ) ( )( )1
1 expLoSPa b a
θθ
=+ − −
3GPP BS-UAV ModelRui Zhang, National University of Singapore
Separately model LoS and NLoS propagations LoS probability and channel modelling parameters are functions of
UAV altitude and horizontal distance
Proposed scenarios: BS-UAV for Urban Macro (UMa), Urban Micro (UMi), and Rural Macro (RMa)
Pros: Comprehensive models for path loss, shadowing and small-scale fading Useful for numerical simulation
Cons: Too complicated for performance analysis and trajectory optimization
UAV Channel Model
3GPP TR 36.777: “Technical specification group radio access network: study on enhanced LTE support for aerial vehicles”, Dec. 2017.
SPAWC 2019 21
( ), , 1
, 2 1 2
2
1.5 m , , , ,
1, 300 m,
LoS ter U
LoS LoS U D U U
U
P H HP P d H H H H
H H
≤ ≤= ≤ ≤ ≤ ≤
Summary of UAV-Ground Channel Channels Rui Zhang, National University of SingaporeUAV Channel Model
SPAWC 2019 22
Channel model Description Pros and Cons
Free-space channelmodel
Channel power inversely proportionalto distance square, no shadowing orsmall-scale fading
Simple, useful for offline UAV trajectory design; oversimplified in urban environment
Altitude-dependentchannel parameters
Channel modelling parameters suchas path loss exponent and shadowingvariance are functions of UAV altitude
Useful for theoretical analysis and offline trajectory design; fails to model the change of propagation environment when UAV moves horizontally
Elevation angle-dependent channelParameters
Channel modelling parameters such as path loss exponent and Rician factor are functions of UAV elevation angle
Useful for theoretical analysis and offline trajectory design; further experimental verification required
Elevation angle dependent probabilistic LoS model
Separately model LoS and NLoSpropagations; LoS probabilityincreases with elevation angle
Useful for theoretical analysis and offline trajectory design; simplified shadowing; model in statistical sense only, may fail in actual environment
3GPP GBS-UAV channel model
Separately model LoS and NLoSpropagations; LoS probability andchannel modelling parameters areboth functions of altitude andhorizontal distance
Comprehensive models for path loss, shadowing and small-scale fading; useful for numerical simulations but too complicated for analysis or UAV trajectory optimization
UAV Energy Consumption ModelUAV 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 results, e.g., 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 Propulsion power as a function of speed and acceleration
Rui Zhang, National University of Singapore
Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wingUAV,” IEEE Trans. Wireless Commun., Apr. 2019.
Y. Zeng and R. Zhang, "Energy-Efficient UAV Communication with Trajectory Optimization," IEEE Trans. Wireless Commun., June 2017.
SPAWC 2019 23
Energy Model Comparison: Straight and level flightUAV Energy Model
Fixed-Wing Rotary-Wing
Convexity with respect to 𝑉𝑉 Convex Non-convex
Components Induced and parasite Induced, parasite, and blade profile
𝑉𝑉 = 0 Infinity Finite
Rui Zhang, National University of Singapore
SPAWC 2019 24
Fixed-Wing Rotary-Wing
Energy Model with General Level Flight (Fixed-Wing)UAV Energy Model
Change in kinetic energyWork required to overcome air resistance
Only depends on speed and centrifugal acceleration (causing heading change)
Independent of actual location or tangential acceleration (causing speed change)
Work-energy principle interpretation
𝒂𝒂(𝑡𝑡)
𝒗𝒗(𝑡𝑡)
𝒂𝒂⊥
(𝑡𝑡)
𝒂𝒂||(𝑡𝑡)
𝒂𝒂⊥𝟐𝟐 (𝑡𝑡)
Rui Zhang, National University of Singapore
SPAWC 2019 25
UAV Communication: Performance MetricPerformance Metric
Signal to interference-plus-noise ratio (SINR) Outage/coverage probability Communication throughput/delay Spectral/energy efficiency All dependent on UAV location/trajectory
Rui Zhang, National University of Singapore
SPAWC 2019 26
Desired signal
Interference
(a) UAV as a transmitter (b) UAV as a receiver
2ter aer
( )( )( )
kk
k
SI I
γσ−=
+ +qQQ 2
ter aer
( )( )( ) ( )
kk
k
SI I
γσ
=+ +
Joint Trajectory-Communication Optimization: Generic Formulation
𝒒𝒒(𝑡𝑡): trajectory
𝒓𝒓(𝑡𝑡): commun. resource
U: utility functions, e.g., communication rate, SINR, coverage probability, spectrum/energy efficiency
fi: trajectory constraints, e.g., speed constraint, obstacle/collision avoidance gi: communication resource constraints, e.g., power, bandwidth hi: coupled constraints, e.g., interference temperature, minimum SINR
requirement
Mathematical Formulation Rui Zhang, National University of Singapore
SPAWC 2019 27
Practical Constraints on UAV Trajectory
Maximum/minimum altitude:
Initial/final status:
Maximum/minimum speed:
Maximum acceleration:
Obstacle avoidance:
Collision avoidance:
No-fly zone (cubic):
Mathematical Formulation Rui Zhang, National University of Singapore
SPAWC 2019 28
min 3 max[ ( )] ,H t H t≤ ≤ ∀q
(0) , ( )I FT= =q q q q
min max|| ( )|| , ,V t V t≤ ≤ ∀v
max|| ( )|| , ,t a t≤ ∀a
1|| ( ) || ,t D t− ≥ ∀q r
2|| ( ) ( )|| , ,k kt t D k k t′ ′− ≥ ∀ > ∀q q6
1
( ) ,Ti i
i
t b t=
≥ ∀a qU
Outline Introduction
UAV Communication Fundamentals
Challenges and Promising Solutions UAV-assisted wireless communication (UAV as BS/relay) Basic models Recent results Joint trajectory and communication optimization Energy-efficient UAV communication
Cellular-connected UAV (UAV as user/terminal)
Conclusions
Rui Zhang, National University of Singapore
SPAWC 2019 29
UAV-Assisted Communications: Basic Models
Downlink
v
u1
s1
v1v2
s2
u2
Multi-UAV Interference Channel
Interference
S1 D1
s1s1
Relaying
u1 u2
s
v
Multicasting
u1 u2
s1s2
v
u1 u2
s1 s2
Uplink
v
UAV-Assisted Wireless Communications Rui Zhang, National University of Singapore
SPAWC 2019 30
Performance analysis Static UAV platform
• Deterministic modelling of UAV location• Stochastic modelling of UAV location: 2D/3D Poisson Point Process; Binomial
Point Process for small-size UAV network Flying UAV platform
• Deterministic modelling of UAV trajectory: circular/straight• Stochastic modelling of UAV trajectory: stochastic trajectory process
UAV placement No user location information (ULI): maximize covered area Perfect ULI: maximize number of covered users, communication throughput,
minimize number of UAVs given per-user requirement Partial ULI
Joint trajectory and communication optimization Exploiting new design DoF of UAV trajectory optimization
Energy-efficient UAV communication
Recent Results: An Overview UAV-Assisted Wireless Communications Rui Zhang, National University of Singapore
SPAWC 2019 31
Probabilistic LoS Channel model Large-scale channel power model for LoS and NLoS conditions
LoS probability:
Expected channel gain:
Exploiting UAV Mobility: How Much Can We Gain?
d2D
HU
V
𝜃𝜃
d
UAV flies towards the ground terminal
Double effects to improve the channel quality: Shorter link distance Less signal obstruction
𝜅𝜅 < 1: additional attenuation for NLoS
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 32
( ) 0
0
, LoS Link , NLoS Link
dd
d
α
α
ββ
κβ
−
−
=
( ) ( )( )1
1 expLoSPa b a
θθ
=+ − −
( ) ( ) ( )( )0 01LoS LoSE d P d P dα αβ θ β θ κβ− −= + −
0 5 10 15 20 25 30 35 40 45 50
t (s)
-140
-135
-130
-125
-120
-115
-110
-105
-100
-95
Path
loss
(dB)
LoS
NLoS
Exploiting UAV Mobility: How Much Can We Gain?
0 5 10 15 20 25 30 35 40 45 50
t (s)
-140
-135
-130
-125
-120
-115
-110
-105
-100
-95
Aver
age
chan
nel p
ower
gai
n (d
B)
Channel gain for LoS and NLoSLoS probability
Expected channel gain
Initial distance d2D 1000 m
UAV altitude Hu 100 m
Flying speed v 20 m/s
Path loss exponent α 2.3
Reference channel gain β0
-50 dB
Probabilistic LoSmodel parameters
𝑎𝑎 = 10, 𝑏𝑏 = 0.6,𝜅𝜅 = 0.01
40 dB
23 dB
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 33
0 5 10 15 20 25 30 35 40 45 50
t (s)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
LoS
prob
abilit
y
Exploiting UAV Mobility for Communication: Key Points
Moving UAV closer to ground terminals brings significant performance gain, beyond the conventional communication design
Main considerations: communication delay and UAV energy consumption
Useful techniques for trajectory and communication co-design Graph theory (e.g., travelling salesman problem, shortest-path
problem) Trajectory quantization (time/path discretization) Optimization techniques (block-coordinate descent, successive
convex approximation, machine learning, etc.)
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 34
Path Planning: Travelling Salesman Problem
Travelling salesman problem (TSP): Given 𝐾𝐾 cities and the distances between each pair of cities, find the shortest route that visits each city and returns to the origin city
Complexity by exhaustive search: 𝐾𝐾! (NP hard) Many heuristic and optimal algorithms (up to tens of thousands of cities)
have been proposed
u1 u2
s1 s2
Uplink/downlink
v UAV path planning: For UAV-enabled
communications with ground users, determine the optimal flying path to serve them sequentially
Intuition: fly to each ground user as close as possible
s3
u3
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 35
Variations of Travelling Salesman Problem
The standard TSP requires the traveler return to the origin city For UAV communications, the UAV may not necessarily return to the
location where it starts the mission, and the initial and/or final locations may be pre-specified
TSP Variation 1: No return TSP Variation 2: No return, specified initial and final locations 𝒒𝒒0 and 𝒒𝒒𝐹𝐹 TSP Variation 3: No return, specified initial location 𝒒𝒒0, any final location
Trajectory and Communication Co-Design
Y. Zeng, X. Xu, and R. Zhang, “Trajectory design for completion time minimization in UAV-enabled multicasting”, IEEE Trans. Wireless Commun., April 2018.
Rui Zhang, National University of Singapore
SPAWC 2019 36
TSP Variation: No Return
Standard TSP No Return
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 37
No Return, any initial/final No Return, specified initial and final
TSP Variation: Specified Initial and Final Locations Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 38
Travelling Salesman Problem with Neighborhood When the total operation time 𝑇𝑇 is small, the UAV may not be able to visit
all users TSP with neighborhood (TSPN): Given 𝐾𝐾 cities and the neighborhoods of
each city, find the shortest route that visits each neighborhood once A generalization of TSP, also NP-hard
𝒫𝒫: set of all 𝐾𝐾! possible permutations
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 39
( ) ( )
( ) ( )
1{ },{ ( )}min
s.t. 1 ,...,
,
kk kk k
k k k
k
r k
π ππ
π π
+ −
∈ − ≤ ∀
∑qq q
q w
P
Pickup-and-Delivery Problem (PDP) For UAV-enabled multi-pair relaying, determine the UAV flying path subject to Information-causality constraint: UAV needs to first receive data from a
source before forwarding to its destination Pickup-and-Delivery Problem (PDP): A generalization of TSP with precedence
constraints: for each source-destination pair, visit source before destination NP-hard, while algorithms for high-quality solutions exist PDP with neighborhood (PDPN)
S1 S2
s1
s2
Multipair relaying
vs1
D1
D2
s2
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 40
UAV Path Planning with TSPN and PDPN
TSPN PDPN
Trajectory and Communication Co-Design
J. Zhang, Y. Zeng, and R. Zhang, “UAV-enabled radio access network: multi-mode communication and trajectory design,” IEEE Trans. Signal Process., Oct. 2018.
Rui Zhang, National University of Singapore
SPAWC 2019 41
Limitations of TSP/PDP For Trajectory Optimization
Suboptimal trajectory in general: Straight flight between waypoints only, while optimal trajectory for
communication are curved in general Ignores various communication/trajectory constraints:
Rate requirement, interference, obstacle avoidance, maximum/minimum speed, no-fly zone….
Only gives UAV flying path, but trajectory optimization includes both path planning and speed optimization
A general framework: joint UAV trajectory and communication resource allocation optimization, by employing TSP/PDP-based path as initial trajectory Time/path discretization Optimization (block coordinate descent, successive convex
approximation, etc.)
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 42
Joint Trajectory-Communication Optimization: Generic Formulation
𝒒𝒒(𝑡𝑡): trajectory
𝒓𝒓(𝑡𝑡): commun. resource
The continuous-time representation of trajectory involves infinite number of variables
Discretization is necessary for optimization and computation purposes Two discretization methods: time discretization and path discretization
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 43
Time vs. Path Discretization Path discretization: generalized time discretization with variable slot length
Time Discretization Path Discretization
Pros • Equal time slot length• Linear state-space representation• Incorporate maximum
acceleration constraint easily
• Fewer variables if UAV hovers or flies slowly
• No need to know T a priori
Cons • Excessively large number of time slots when UAV moves slowly
• Needs to know T a priori
• More variables if UAV flies with high/maximum speed most of the time
0 𝑇𝑇 = 𝑁𝑁𝛿𝛿𝑡𝑡Time discretization: 𝑇𝑇 must known
𝛿𝛿𝑡𝑡 2𝛿𝛿𝑡𝑡
𝒒𝒒[1] 𝒒𝒒[2] 𝒒𝒒[𝑁𝑁]……
Path discretization:𝑇𝑇 can be unknown𝒒𝒒1 ……𝑇𝑇1 𝑇𝑇2
𝒒𝒒2 𝒒𝒒𝑀𝑀
Trajectory and Communication Co-Design
𝑇𝑇 =∑𝑇𝑇𝑚𝑚
Rui Zhang, National University of Singapore
SPAWC 2019 44
Block Coordinate Descent
Time or path discretization converts the problem into a discrete form The (discrete) joint trajectory and resource optimization problems are
generally com-convex and difficult to solve Block coordinate descent: alternately update one block of variables (say,
trajectory) with the other (resource allocation) fixed. Monotonically converge to a locally optimal solution
Optimize 𝒒𝒒[𝑛𝑛]Optimize 𝒓𝒓[𝑛𝑛]
𝑙𝑙 = 𝑙𝑙 + 1
𝒒𝒒(𝑙𝑙)[𝑛𝑛] 𝒒𝒒(𝑙𝑙+1)[𝑛𝑛]𝒓𝒓(𝑙𝑙+1)[𝑛𝑛]
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 45
Successive Convex Approximation Even with given resource allocation, UAV trajectory optimization is usually non-
convex, and thus difficult to solve Non-concave objective functions: e.g., rate maximization Non-convex constraints: e.g., obstacle/collision avoidance, minimum speed
Successive convex approximation (SCA): local optimization via solving a sequence of convex problems converges to a KKT solution if appropriate local bounds are found
• Convex optimization problem• Solution is feasible to the original
non-convex problem
Non-convex optimization problem
Global concave lower bound
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 46
Successive Convex Approximation
Communication rate maximization:
Minimum speed constraint:
Convex optimization based
on lower bounds
Find global concave lower
bounds
𝑙𝑙 = 𝑙𝑙 + 1
𝐴𝐴𝑘𝑘,𝐵𝐵𝑘𝑘: poisitive coefficients depending on 𝒒𝒒(𝑙𝑙)[𝑛𝑛]
𝒒𝒒(𝑙𝑙)[𝑛𝑛] 𝒒𝒒(𝑙𝑙+1)[𝑛𝑛]
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 47
( )0 ( )2log 1 || [ ] || [ ]
|| [ ]|| ||
||l
k k k kk
A B n nn α
γ + ≥ − − − − −
q w q wq w
( )2 ( ) 2 ( ) ( ) 2min|| [ ]|| || [ ]|| 2 [ ] [ ] [ ]l l T ln n n n n V≥ + − ≥v v v v v
Case Studies
Example 1: Multi-UAV Enabled Wireless Network
Example 2: Energy-Efficient UAV Communication
Q. Wu, Y. Zeng, and R. Zhang, “Joint trajectory and communication design for multi-UAV enabled wireless networks,” IEEE Trans. Wireless Commun., Mar. 2018.
Trajectory and Communication Co-Design
Y. Zeng and R. Zhang, “Energy-Efficient UAV Communication with Trajectory Optimization,” IEEE Trans. Wireless Commun., June 2017.
Rui Zhang, National University of Singapore
SPAWC 2019 48
Multi-UAV Enabled Wireless Network Multi-UAV Enabled Wireless Network
Multi-UAV downlink communications with ground users TDMA for user communication scheduling Problem: maximize the minimum average rate of all users via joint
communication (scheduling, power control) and UAV trajectories optimization
Rui Zhang, National University of Singapore
SPAWC 2019 49
Problem FormulationMulti-UAV Enabled Wireless Network Rui Zhang, National University of Singapore
SPAWC 2019 50
Minimum rate requirement
UAV mobility constraint
TDMA constraints
power constraint
Initial/final location constraint
collision avoidance constraint
Nonconvex, solved by time-discretization and block coordinate descent
Simulation ResultsMulti-UAV Enabled Wireless Network
New Interference-mitigation approach: coordinated multi-UAV trajectory design
Rui Zhang, National University of Singapore
SPAWC 2019 51
Simulation Results: Throughput-Delay Tradeoff
Multi-UAV Enabled Wireless Network
Longer flight period achieves higher max-min throughput, but incurs larger user delay on average
Rui Zhang, National University of Singapore
SPAWC 2019 52
Energy-Efficient UAV CommunicationEnergy-Efficient UAV Communication
UAV energy consumption (fixed-wing):
Aggregate throughput as a function of UAV trajectory
Energy efficiency in bits/Joule:
Rui Zhang, National University of Singapore
SPAWC 2019 53
Energy Efficiency MaximizationEnergy-Efficient UAV Communication
Maximize energy efficiency in bits/Joule via trajectory optimization
Non-convex, solved by time discretization and successive convex approximation (SCA)
Initial/final location constraint
Min./Max. speed constraint
Initial/final velocity constraint
Max. acceleration constraint
Rui Zhang, National University of Singapore
SPAWC 2019 54
Simulation Results: Throughput-Energy Tradeoff Energy-Efficient UAV Communication
Rate-max trajectory: stay as close as possible with the ground terminal Energy-min trajectory: less acute turning EE-max trajectory: balance the two, “8” shape trajectory
Rui Zhang, National University of Singapore
SPAWC 2019 55
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,” IEEE Wireless Communications, Feb. 2019.
Rui Zhang, National University of SingaporeTrajectory and Communication Co-Design
SPAWC 2019 56
Thr
ough
put
EnergyDelay
Thr
ough
put
Del
ay
Energy
Trajectory Optimization: More Comments
Trajectory optimization generally comprises offline and online phases: Offline: Based on given information on user location/channel
statistics/communication requirement, design UAV trajectory based on graph theory and optimization techniques
Online: Adjust UAV trajectory based on real-time measured channel by applying techniques such as radio mapping, reinforcement learning…
Useful techniques for complexity reduction Alternating Direction Method of Multipliers (ADMM) for
distributed/parallel computing Receding horizon (sliding-window) based optimization Machine learning techniques (such as deep learning)
Trajectory and Communication Co-Design Rui Zhang, National University of Singapore
SPAWC 2019 57
Outline Introduction
UAV Communication Fundamentals
Challenges and Promising Solutions UAV-assisted wireless communication (UAV as BS/relay) Cellular-connected UAV (UAV as user/terminal) Main challenges 3GPP study Aerial-ground interference mitigation
Conclusions
Rui Zhang, National University of Singapore
SPAWC 2019 58
Rui Zhang, National University of SingaporeCellular-Connected UAV
Cellular-Connected UAV: Main Challenges High altitude
3D coverage is challenging: existing BS antennas tilted downwards High 3D mobility
Frequent handovers, cell selection Asymmetric downlink/uplink: ultra-reliable CNPC versus high-rate payload data Strong air-ground LoS dominant channel
Pro: High macro-diversity (beneficial for BS association) Con: Severe aerial-ground interference (in both uplink and downlink)
Mainly served by antenna side-lobe with current LTE BS
SPAWC 2019 59
Recent results by 3GPPCellular-Connected UAV
Interference detection Uplink interference mitigation
Uplink power control with altitude-dependent compensation factors 𝛼𝛼UE
Full-dimensional MIMO (3D beamforming) Directional antenna at UE
Downlink interference mitigation Intra-site joint transmission (CoMP) Enhanced initial access Coordinated data and control transmission
Mobility Refining handover parameters based on airborne status, path information…
3GPP TR 36.777: “Technical specification group radio access network: study on enhanced LTE support for aerial vehicles”, Dec. 2017.
Rui Zhang, National University of Singapore
SPAWC 2019 60
tx max 10 RB 0 UEmin{ ,10log ( ) TPL}P P M P α= + + ⋅
Rui Zhang, National University of SingaporeCellular-Connected UAV
Reshaping Cellular Networks to Cover the Sky
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: Aerial-ground NOMA 3D beamforming QoS-aware trajectory design
SPAWC 2019 61
Y. Zeng, Q. Wu, and R. Zhang, “Access from the Sky: a tutorial on UAV communications for 5G and beyond,’’ submitted to Proceedings of the IEEE, Invited Paper, available on arXiv
Inter-Cell Interference Coordination (ICIC) with UAVs Cellular-Connected UAV
ICIC region is much larger than for terrestrial users due to LoS channels Dedicated orthogonal channels for UAVs: spectrum inefficient, especially when
terrestrial user density is high
Rui Zhang, National University of Singapore
SPAWC 2019 62
Aerial-Ground NOMA
W. Mei and R. Zhang, “Uplink cooperative NOMA for cellular-connected UAV,” IEEE J. Sel. Topics Sig. Process, 2019 (to appear).
Non-Orthogonal Multiple Access (NOMA) Non-cooperative NOMA: each co-channel BS employs local interference
cancelation only, inefficient due to large number of interfered BSs Cooperative NOMA: idle BSs decode UAV message first and forward to
adjacent co-channel BSs for interference cancelation
Cellular-Connected UAV Rui Zhang, National University of Singapore
SPAWC 2019 63
M: BS Cooperation Size
3D Beamforming BS array configuration: fixed pattern (downtilted) vs 3D beamforming
Y. Zeng, J. Lyu, and R. Zhang, “Cellular-connected UAV: potentials, challenges and promising technologies,” IEEE Wireless Communications, Feb. 2019.
Cellular-Connected UAV Rui Zhang, National University of Singapore
3GPP urban macro (UMa) scenario Total # of terrestrial and UAV users: 20
SPAWC 2019 64
QoS-Aware Trajectory DesignCellular-Connected UAV
Exploit UAV trajectory to improve communication performance (e.g., SINR), based on (statistical) knowledge of channel/interference spatial distribution
Equivalent to constrained shortest path problems in graph theory
S. Zhang, Y. Zeng, and R. Zhang, “Cellular-enabled UAV communication: a connectivity-constrained trajectory optimization perspective,” IEEE Trans. Commun. Mar. 2019 (Invited Paper)
Rui Zhang, National University of Singapore
BS coverage region 𝛾𝛾1 < 𝛾𝛾2
SPAWC 2019 65
Outline
Introduction
UAV Communication Fundamentals
Challenges and Promising Solutions: UAV-assisted wireless communication (UAV as BS/relay) Cellular-connected UAV (UAV as user/terminal)
Conclusions
Rui Zhang, National University of Singapore
SPAWC 2019 66
ConclusionsRui Zhang, National University of Singapore
Integrating UAVs into 5G and beyond: a promising paradigm to embrace the new era of Internet-of-drones (IoD)
Cellular-Connected UAV: UAV as new aerial user/terminal
UAV-Assisted Wireless Communication: UAV as new mobile BS/relay/data collector
Main challenges: Air-ground channel modelling 3D coverage Joint trajectory and communication design Energy efficiency Aerial-ground interference management
Much more to be done…
Conclusions
SPAWC 2019 67
Rui Zhang, National University of SingaporeExtensions/Future Directions
Extensions/Future Directions
UAV-BS/UE channel modelling and experimental verification 3D network modelling and performance analysis General UAV energy model and energy-efficient communication Security issues in UAV communications Massive MIMO/mmWave for UAV communications Joint offline-online UAV trajectory design with QoS constraints Cellular-enabled UAV swarm communications UAV communications with limited wireless backhaul UAV meets wireless power/energy harvesting/caching/edge
computingMachine learning/AI for UAV communications and networking ……
SPAWC 2019 68