TY - JOUR

T1 - The Optimal and the Greedy: Drone Association and Positioning Schemes for Internet of UAVs

AU - Hammouti, Hajar El

AU - Hamza, Doha R.

AU - Shihada, Basem

AU - Alouini, Mohamed-Slim

AU - Shamma, Jeff S.

N1 - KAUST Repository Item: Exported on 2021-04-02

PY - 2021

Y1 - 2021

N2 - This work considers the deployment of unmanned aerial vehicles (UAVs) over a pre-defined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum-rate of the users. The agents’ utility in the potential game is their marginal contribution to the global welfare or their socalled wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

AB - This work considers the deployment of unmanned aerial vehicles (UAVs) over a pre-defined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum-rate of the users. The agents’ utility in the potential game is their marginal contribution to the global welfare or their socalled wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

UR - http://hdl.handle.net/10754/666236

UR - https://ieeexplore.ieee.org/document/9392003/

U2 - 10.1109/JIOT.2021.3070209

DO - 10.1109/JIOT.2021.3070209

M3 - Article

SP - 1

EP - 1

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2372-2541

ER -