This article considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet-of-Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the 3-D UAV placements, transmit power, and cloud resources. This represents a challenging, nonconvex, and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems, namely, 2-D UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global $K$ -means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending that data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this paper, a MEC scenario in 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the non-orthogonal multiple access (NOMA) protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency trade-off, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation (SCA). Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency trade-off of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

Vehicular networks are known for their dynamic topology, high mobility, and frequent disconnections. Unmanned aerial vehicles (UAVs) have been recently used as instant communication relays to bridge the communication gaps between terrestrial vehicles to improve connectivity in vehicular networks and overcome the aforementioned problems. Despite the existing work in the literature where each vehicle connects directly to UAVs, this work studies how clustering and different densities of vehicles affect delay and energy efficiency in a UAV-based vehicular network integrated with 5G technology. Consequently, this work addresses the problem of UAV enabling vehicular ad-hoc networks (VANETs) in a highway scenario, where UAVs serve as an effective complement to forward data packets between vehicles in the absence of sufficient fixed infrastructures in an emergency situation. The main objective of this work is to minimize the delay while maximizing the energy efficiency by minimizing the power consumption and maximizing the total data rate under realistic conditions, while nonorthogonal multiple access (NOMA) is also adopted as an alternative answer for the effective utilization of limited bandwidth. The free-flowing traffic follows a Poisson stochastic process where each vehicle is assigned a random speed selected from a truncated Gaussian distribution. To this end, a novel modification of fast global $K$ -means is adopted to partition vehicles, allowing communication between clusters by vehicle-to-vehicle links, while data packets between clusters are relayed through UAVs. By computing the convex approximation of the objective function and the constraints, the original problem with the mixed-integer, nonconvex, and nonlinear form is solved by the proposed iterative inner penalty function algorithm. Finally, extensive simulations are conducted to validate the superiority of the proposed method in terms of various metrics. The results indicate that the relaying task in the proposed UAV-assisted VANET based on 5G technology is perfectly suited to enhance the network connectivity.

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

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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.