Multi-UAV Placement and User Association in Uplink MIMO Ultra-Dense Wireless Networks

This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. We aim to jointly maximize the number of served IMDs, the user scheduling, the number of UAVs, and their $3$ D placement. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. Due to its non-convexity, we break the problem into two subproblems. We propose a machine-learning-based algorithm for the first subproblem, i.e., optimizing the UAVs' number and $3$ D placements, and the user association. Unlike existing literature, our algorithm achieves low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the $\ell_p$ -norm concept and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until convergence, and a near-optimal solution is achieved. Moreover, the computational complexity of the proposed scheme is analyzed. Regarding fast convergence and low computational complexity of the proposed algorithm, we confirm its superior performance through simulation results.