UAV-Mounted Mobile Base Station Placement via Sparse Recovery

Abstract

In order to deploy minimum number of unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) to service all given ground terminals, this paper proposes an MBS placement based on sparse recovery (MBS-PBSR) algorithm. By exploiting the sparsity inherent in the differences between any two dedicated MBSs, the problem of UAV-mounted MBS placement could be formulated as an 0-norm constrained optimization problem, which is then be solved by the reweighted 1-norm method. Subsequently, the resulted solutions to the MBS placement are adjusted by the iterative redundant circle deletion algorithm, eventually leading to the redundant MBSs removal as much as possible. Simulation results demonstrate that our proposed MBS-PBSR algorithm works well with affordable computational complexity, and is nearly optimum in the sense of the number of deployed UAV-mounted MBSs. INDEX TERMS Unmanned aerial vehicle, mobile base station, sparse recovery.

3D Placement of a New Tethered UAV to UAV Relay System for Coverage Maximization

“…An algorithm to maximize the downlink sum-rate of the network was proposed in [15]. An algorithm for UAV placement based on sparse recovery was presented in [16]. However, all these works consider only the power constraints of the communication link between a UAV and the ground user mobile station (MS) and don't consider the power constraints of the communication link between a UAV and the BS.…”

“…UAV Placement for Pros Cons [6] Coverage maximization Jointly optimizes the 3D UAV placement and path loss compensation factor References [6][7][8][9][10][11][12][13][14][15][16] consider only the power constraints of the communication link between UAV and the ground user mobile station (MS) but do not consider the power constraints of the communication link between a UAV and the BS [7] Minimizes the total transmit power required to provide wireless coverage for indoor users [8] Maximizes the number of covered users with minimum transmission power [9] Maximizes the number of served users with different quality-of-service requirements [10] finds the optimum UAV altitude [11] Throughput maximization…”

3D Placement of a New Tethered UAV to UAV Relay System for Coverage Maximization

“…However, all these works consider only the power constraints of the communication link between UAV and the ground user mobile station (MS) and don't consider the power constraints of the communication link between UAV and the BS. Otherwise, researches that consider both links is presented in [14][15][16][17][18][19][20][21][22]. [14][15][16][17][18] are proposed for throughput maximization.…”

“…Otherwise, researches that consider both links is presented in [14][15][16][17][18][19][20][21][22]. [14][15][16][17][18] are proposed for throughput maximization. 3D placement of UAV as a relay station for maximizing the average achievable rate through the one-dimensional linear search is proposed in [14].…”

“…In [15], the optimization problem is formulated to maximize the system throughput. An algorithm to find the UAV optimal position based on LOS information to maximize the end-to-end throughput is proposed in [16] . [17] explores the relationship between system throughput and placement of a UAV acting as a communication relay.…”