Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

By Qingqing Wu, Yong Zeng, Rui Zhang. IEEE Transactions on Wireless Communications, 2017

1,751 citations recorded by the source corpus.

Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV’s trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3b30b5b5cc861be94387905ad013ac9f4c427a98

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Citations in the corpus

UAV-Assisted Emergency Networks in Disasters (2019, background): “In [5], some excellent work was done by Wu et al.”
Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, methodology): “Compared with the communication rate expression for the special free-space channel model as widely assumed in prior works [24], [26], [28], the constraint in (11) is more general that differs mainly in two aspects.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, methodology): “The work in [36] studied the joint user scheduling and UAV trajectory design to maximize the minimum rate of ground users in a multi-UAV enabled wireless network.”
Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting (2018, methodology): “This technique has been later adopted for UAV trajectory optimization in various other setups, including the energy efficiency maximization for UAV-enabled communication [28], throughput maximization for UAV-enabled multi-user downlink communication [29], and sensor energy minimization in UAV-enable”
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, methodology): “The work in [17] jointly optimized the trajectory, multi-user scheduling and power control for multiple UAVs to maximize the minimum rate of ground users.”
Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication (2021, background): “, [30], [33], [34]) for gaining essential insights on the placement/traejctory design.”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “The UAV-assisted communication network has been investigated in works [20]–[22].”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, background): “[6] proposed a suboptimal joint trajectory, power allocation, and user scheduling algorithm for maximization of the minimum user throughput in multi-UAV systems.”
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks (2018, methodology): “Though resource allocation schemes such as transmit power and subchannels were considered for UAV-enabled communication networks in [16] and [17], the prior studies focused on time-independent scenarios.”
Physical-Layer Security of 5G Wireless Networks for IoT: Challenges and Opportunities (2019, background): “only with ground nodes at fixed or quasi-static locations, these works [96], [97] exploited the high mobility of the UAVs to proactively establish favorable and degraded channels for the”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, methodology): “We consider a UAV-aided MEC system, where K = 8 ground users are distributed in a 2-D area of 2×2 km2 randomly [25].”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, methodology): “For certain scenarios, such as rural areas with few blockages and scatterers and UAVs flying at high altitudes to maintain LoS links, the free-space path loss model provides a reasonable approximation and has been widely used in the existing works [109]–[111].”
Routing in Flying Ad Hoc Networks: A Comprehensive Survey (2020, neutral): “In [52], considering a multiUAV-assisted wireless network, the authors investigated UAV’s trajectory and power control jointly with multi-user scheduling and association to maximize the minimum downlink throughput of terrestrial users.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “As a result, free-space channel model has been widely assumed in early works on ofﬂine UAV trajectory optimization in communication systems [16,38,39].”
Common Throughput Maximization in UAV-Enabled OFDMA Systems With Delay Consideration (2018, background): “This is due to the fundamental tradeoff between the system throughput and the user delay/MRR constraint [7], [20], [21], which is in accordance with Theorem 1.”
Throughput Maximization for UAV-Enabled Integrated Periodic Sensing and Communication (2022, background): “The UAV’s horizontal location is denoted by q[n] = [qx[n], qy[n]] , where n ∈ N , and the UAV is assumed to fly at a constant altitude of H m subject to air traffic control [36].”
MIMO Capacity Characterization for Movable Antenna Systems (2022, background): “…globally lower-bounded by its first-order Taylor expansion at arbitrary point, we can derive the following lower bound on f ( r m ) H B m f ( r m ) with given local point r in the i th iteration of SCA [32]: where r ∈ R 2 is a constant vector which represents the value of r m in the i th iteration.”
Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots (2018, methodology): “In [5], considering the whole trajectory allows the authors to jointly optimize scheduling and user association, whereas the authors of [4] and [6] consider the power consumption of the UAV and attached BS in addition to the QoS.”