Optimal Positioning of Flying Relays for Wireless Networks Junting - - PowerPoint PPT Presentation

Optimal Positioning of Flying Relays for Wireless Networks

Junting Chen1 and David Gesbert2

1Ming Hsieh Department of Electrical Engineering, University of Southern California, USA 2Department of Communication Systems, EURECOM, Sophia-Antipolis, France

Acknowledgement

This research was supported by the ERC under the European Union’s Horizon 2020 research and innovation program (Agreement no. 670896)

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Relaying from the Air

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Ground relays:

• Constrained / fixed positions
• Shadowing
• Non-adaptive to user

mobility / ad-hoc loading Unmanned aerial vehicle (UAV) relays:

• Flexible positions
• Shadowing avoidance
• User mobility adaptive

Feasibility / opportunity:

• Decreasing fabrication cost
• mmWave applications

Microscopic Viewpoint of UAV Relaying

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Traditional application scenario:

• Establish connectivity from tens

to hundreds kilometers away

• Path loss dominant
• Service to an area

Application to cellular networks:

• Fill the coverage / capacity hole

within 1 kilometers

• Shadowing dominant
• Avoid propagation blockage
• Service to a selected group of users

Key Challenge: How to model the air-to-ground propagation?

Traditional relay problem

§ Relay positions are fixed § Communication channels are known or can be estimated

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UAV positioning problem

§ Relay positions are to be optimized § Comm channels are unknown, before the UAV is in position § UAV-user, UAV-BS channels are functions of the UAV position, and the environment (e.g., blockage) etc.

Macroscopic Propagation Models versus Fine-grained Structure Exploitation

Existing works usually based on macroscopic propagation models

§ Line-of-sight (LOS) propagation assumption § Probability model for LOS propagation

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5 [Hourani14] A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP Altitude for Maximum Coverage”, IEEE Comm. Lett., 2014. [Mozaffari16] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Optimal Transport Theory for Power-Efficient Deployment of Unmanned Aerial Vehicles”, IEEE ICC, 2016.

Limitations

§ Not performance guaranteed for individual users! E.g., a QoS-demanding user in the “shadow of a building” (say, in low mobility case) § The local fine-grained terrestrial structure has not been exploited!

UAV Positioning in Dense Urban Areas

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To place a UAV to relay the signal to a QoS-demanding user on the ground

Where to Place the UAV Relay?

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View the dense urban area from the top Received power of UAV-user link End-to-end capacity of BS-UAV-user link A possibly good UAV relay position

Simplest scenario: To increase the end-to-end transmission rate to one target user on the ground, by placing the UAV relay to the best position.

Problem Formulation

End-to-end rate maximization for a simple single user case where More generally,

§ Challenge 1: Simple mathematical description on the channels gB and gD in terms of the drone position, such that the fine-grained environment structure is preserved (i.e., LOS/NLOS). § Challenge 2: Efficient algorithm to find the optimal UAV position xD.

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maximize

xD

min {rB(xD), rD(xD)} rB(xD) = log2 ⇣ 1 + PB · gB(xD) ⌘ rD(xD) = log2 ⇣ 1 + PD · gD(xD) ⌘ minimize

x∈R3

F(gU(x), gB(x))

Ray-tracing Propagation Model with Segmented Approximation

Classical log-distance model

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10 log10

• gU(x)
• = 10 log10(β) 10α log10
• kx xUk
• + ξ

Shadowing (LOS/NLOS, etc), reflection, diffraction, etc.

Conventional relay literature,

• btained by online estimation

Recent UAV literature: LOS model,

• r probabilistic LOS model

Ray-tracing Propagation Model with Segmented Approximation

Classical log-distance model Segmented model

§ Partition the space of (x, xU) into K disjoint segments, each corresponding to a type of propagation (LOS, NLOS, etc.) where

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gU(x) = X

k

gk(x)I{(x, xU) ∈ Dk} 10 log10

• gU(x)
• = 10 log10(β) 10α log10
• kx xUk
• + ξ

Shadowing (LOS/NLOS, etc), reflection, diffraction, etc. Propagation segment, e.g., LOS/NLOS

10 log10

• gk(x)
• = 10 log10(βk) 10αk log10
• kx xUk
• ξk + ˜

ξk

ignored

• Q. Feng, J. McGeehan, E. K.

Tameh, and A. R. Nix, “Path loss models for air-to-ground radio channels in urban environments,” in Proc. IEEE Semiannual Veh.

• Technol. Conf., vol. 6, 2006,
• pp. 2901–2905.

Geometry Property

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Assumption / restriction

§ Assume LOS propagation for the BS-UAV link. § For the UAV-user link, propagation parameters and the segments known perfectly § Propagation segments arranged in order § UAV flies at a fixed height

Intuition:

§ the key is to search for the LOS propagation segment (D1) § it will be convenience to work in the polar-coordinate system

ρ

Special case for two segments: k = 1 for LOS and k = 2 for NLOS

§ When UAV is in NLOS, search along the contour of § When UAV is in LOS, increases (moving away from the user) § Repeat until some stopping criterion is met Theorem: The continuous trajectory constructed by the above algorithm finds the global optimal UAV position

Algorithm

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F(g1(x), g1(x)) = C ρ

UAV Search Trajectory

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• The search trajectory scales only linearly

with L (user-BS distance)

• Significant improvement over exhaustive

search L2

L

Numerical Results

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10,000 random user locations, buildings 5 - 45 meters, UAV 50 meter height Simple UAV positioning: Only search

• ver the BS-user line segment

Offline UAV positioning: First, learn the empirical LOS probability function Then, compute the optimal UAV position based on channel gain Direct BS-user link: Directly BS à user transmission without UAV relaying

P(LOS, θ) = ⇥ 1 + a exp(−b[θ − a]) ⇤−1

Significant gains for cell edge users

Conclusions

Problem: UAV positioning for relaying between a BS to a ground user

§ Dense urban scenario § Key challenge to avoid obstacle blockage § UAV fixed height (2D optimization)

Applications:

§ Quality-demanding service, first aid (road side assistant), surveillance, etc. § Communications in very high frequencies

Segmented propagation model

§ Simple; able to exploit the structure for blockage avoidance

Positioning algorithm

§ Online algorithm § Exploit the segmented propagation model § Global optimal convergence

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