Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users in various applications (e.g., in emergency situations). This paper considers a UAV-enabled wireless network, in which multiple UAVs are deployed as aerial base stations to serve users distributed on the ground. Different from prior works that ignore UAVs’ backhaul connections, we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls. We also consider that the air-to-ground access links from UAVs to users and the air-to-air backhaul links among UAVs are operated over orthogonal frequency bands. Under this setup, we aim to maximize the common (or minimum) throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs, by optimizing the UAVs’ deployment locations, jointly with the bandwidth and power allocation of both the access and backhaul links. However, the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally. To tackle this issue, we use the techniques of alternating optimization and successive convex programming to obtain a locally optimal solution. Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.

[1]  Xiaoli Xu,et al.  Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting , 2018, IEEE Transactions on Wireless Communications.

[2]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[3]  Halim Yanikomeroglu,et al.  Efficient 3-D placement of an aerial base station in next generation cellular networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[4]  Rui Zhang,et al.  Cyclical Multiple Access in UAV-Aided Communications: A Throughput-Delay Tradeoff , 2016, IEEE Wireless Communications Letters.

[5]  Rui Zhang,et al.  Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network , 2017, IEEE Wireless Communications Letters.

[6]  Jie Xu,et al.  Capacity Characterization of UAV-Enabled Two-User Broadcast Channel , 2018, IEEE Journal on Selected Areas in Communications.

[7]  Walid Saad,et al.  Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs , 2015, IEEE Transactions on Wireless Communications.

[8]  David Gesbert,et al.  Optimal positioning of flying relays for wireless networks: A LOS map approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[9]  Rui Zhang,et al.  Wireless communications with unmanned aerial vehicles: opportunities and challenges , 2016, IEEE Communications Magazine.

[10]  Xiang Cheng,et al.  Three Dimensional Modeling and Space-Time Correlation for UAV Channels , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[11]  Jie Xu,et al.  UAV-Enabled Cellular Networks with Multi-Hop Backhauls: Placement optimization and Wireless Resource Allocation , 2018, 2018 IEEE International Conference on Communication Systems (ICCS).

[12]  Walid Saad,et al.  Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[13]  Jie Xu,et al.  UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization , 2017, IEEE Transactions on Wireless Communications.

[14]  Jie Xu,et al.  UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Region Characterization , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[15]  Jie Xu,et al.  Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks , 2018, IEEE Internet of Things Journal.

[16]  David Gesbert,et al.  Learning radio maps for UAV-aided wireless networks: A segmented regression approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[17]  Joonhyuk Kang,et al.  Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning , 2016, IEEE Transactions on Vehicular Technology.

[18]  Shuowen Zhang,et al.  Joint Altitude and Beamwidth Optimization for UAV-Enabled Multiuser Communications , 2017, IEEE Communications Letters.

[19]  Wei Zhang,et al.  Spectrum Sharing for Drone Networks , 2017, IEEE Journal on Selected Areas in Communications.

[20]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[21]  Rui Zhang,et al.  Throughput Maximization for UAV-Enabled Mobile Relaying Systems , 2016, IEEE Transactions on Communications.

[22]  Qiang Ni,et al.  Drone-Aided Communication as a Key Enabler for 5G and Resilient Public Safety Networks , 2018, IEEE Communications Magazine.

[23]  Rui Zhang,et al.  Placement Optimization of UAV-Mounted Mobile Base Stations , 2016, IEEE Communications Letters.

[24]  Ling Qiu,et al.  Capacity of UAV-Enabled Multicast Channel: Joint Trajectory Design and Power Allocation , 2018, 2018 IEEE International Conference on Communications (ICC).