Optimal Positioning of Flying Relays for Wireless Networks

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1 Optimal Positioning of Flying Relays for Wireless Networks Junting Chen 1 and David Gesbert 2 1 Ming Hsieh Department of Electrical Engineering, University of Southern California, USA 2 Department 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 ) 9 July

2 Relaying from the Air 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 9 July

3 Microscopic Viewpoint of UAV Relaying 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 9 July

4 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 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. 9 July

5 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 [Hourani14] A. Al-Hourani, S. Kandeepan, and S. Lardner, Optimal LAP Altitude for Maximum Coverage, IEEE Comm. Lett., [Mozaffari16] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, Optimal Transport Theory for Power-Efficient Deployment of Unmanned Aerial Vehicles, IEEE ICC, 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! 9 July

6 UAV Positioning in Dense Urban Areas To place a UAV to relay the signal to a QoS-demanding user on the ground 9 July

7 Where to Place the UAV Relay? 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. A possibly good UAV relay position View the dense urban area from the top Received power of UAV-user link End-to-end capacity of BS-UAV-user link 9 July

8 Problem Formulation End-to-end rate maximization for a simple single user case where More generally, maximize x D min {r B (x D ),r D (x D )} r B (x D ) = log 2 1+P B g B (x D ) r D (x D ) = log 2 1+P D g D (x D ) minimize x2r 3 F (g U (x),g B (x)) Challenge 1: Simple mathematical description on the channels g B and g D 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 x D. 9 July

9 Ray-tracing Propagation Model with Segmented Approximation Classical log-distance model 10 log 10 g U (x) = 10 log 10 ( ) 10 log 10 kx x U k + Recent UAV literature: LOS model, or probabilistic LOS model Shadowing (LOS/NLOS, etc), reflection, diffraction, etc. Conventional relay literature, obtained by online estimation 9 July

10 Ray-tracing Propagation Model with Segmented Approximation Classical log-distance model 10 log10 gu (x) = 10 log10 ( ) 10 log10 kx xu k + Segmented model Shadowing (LOS/NLOS, etc), reflection, diffraction, etc. k + k Partition the space of (x, xu) into K disjoint segments, each corresponding to a type of propagation (LOS, NLOS, etc.) ignored X gu (x) = gk (x)i{(x, xu ) 2 Dk } Propagation segment, k where e.g., LOS/NLOS 10 log10 gk (x) = 10 log10 ( k) 10 k log10 kx xu k 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 July

11 Geometry Property 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 (D 1 ) it will be convenience to work in the polar-coordinate system 9 July

12 Algorithm 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 F (g 1 (x),g 1 (x)) = C Theorem: The continuous trajectory constructed by the above algorithm finds the global optimal UAV position 9 July

13 UAV Search Trajectory L The search trajectory scales only linearly with L (user-bs distance) Significant improvement over exhaustive search L 2 9 July

14 Numerical Results 10,000 random user locations, buildings 5-45 meters, UAV 50 meter height Simple UAV positioning: Only search over the BS-user line segment Offline UAV positioning: First, learn the empirical LOS probability function P(LOS, ) = 1+a exp( b[ a]) 1 Then, compute the optimal UAV position based on channel gain Direct BS-user link: Directly BS à user transmission without UAV relaying Significant gains for cell edge users 9 July

15 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 9 July