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 the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the$\ell _p$-norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global${K}$-means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.