In this project, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network’s sum-rate. Our approach is referred to as “Learn-As-You-Fly” algorithm (LAYF). LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted.
Prof. Mohamed-Slim Alouini, Electrical Engineering, KAUST, KSA
Dr. Hajar El-Hammouti, Electrical Engineering, KAUST, KSA
Prof. Mustafa Benjilali, INPT, Morocco
Liang Zhang, Electrical Engineering, KAUST, KSA
H. El-Hammouti, M. Benjillali, B. Shihada, and M.-S. Alouini, "Learn-As-You-Fly: A Distributed Algorithm for Joint 3D Placement and User Association in Multi-UAVs Networks", IEEE Transactions on Wireless Communications, Accepted, 2019. [PDF]
H. El-Hammouti, M. Benjilali, B. Shihada, and M.-S. Alouini "A Distributed Mechanism for Joint 3D Placement and User Association in UAV-Assisted Networks", in Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 1 - 7, 2019. [PDF]