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Unmanned aerial vehicles (UAV) assisted edge computing has risen as an assuring technique to accommodate ubiquitous edge computation for resource‐limited devices. Thus, this paper proposes an approach to maximize the latency fairness in a UAV‐assisted multi‐access edge computing (MEC) system. To maximize latency fairness, the authors focus on minimizing the maximum latency experienced among the users. In here, multiple ground users (GUs) offload their tasks to MEC UAV in the absence or unavailability of ground servers due to a disaster or heavy traffic where an iterative algorithm is proposed to minimize the maximum latency among the users subject to minimum control link rate and total power constraints. Sequentially, the UAVs' 3D location, offloading ratio, GUs' transmit power and GUs' computational capacity are optimized. The location of the UAV is optimized by using the novel approach, guided pattern search algorithm while the altitude of the UAV is optimized by analyzing the elevation angle dependant behaviour of the channel gain. A simple approach is utilized for optimizing the offloading ratio of the users by considering the problem as minimizing the point‐wise maximum of two convex functions while the bisection method is used to optimize the power allocation. Numerical simulation results illustrate that the proposed approach outperforms other baseline approaches in convergence, minimizing the maximum latency and maximizing and maintaining the fairness among the GUs. Furthermore, it is proved that the guided pattern search algorithm converges at least 3.5 times better while the proposed combined optimization gives 400% fairness gain, in comparison with the baseline approach. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have recently been envisaged as an enabling technology of 5G. UAVs act as an intermediate relay node to facilitate uninterrupted, high quality communication between information sources and their destination. However, UAV energy management has been a major issue of consideration due to limited power supply, affecting flight duration. Thus, we introduce in this paper a unified energy management framework by resorting to wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and self-interference (SI) energy harvesting (EH) schemes, in cooperative relay communications. In our new technique, UAVs are deployed as relays equipped with a decode and forward protocol and EH capability operating in a full-duplex (FD) mode. The UAV assists information transmission between a terrestrial base station and a user. The UAV's transmission capability is powered exclusively by the energy harvested from WPT, radio frequency signal transmitted from the source via time-switching SWIPT protocol and SI exploitation. We improve the overall system throughput by the use of FD based UAV-assisted cooperative system. In this proposed system, we formulate two optimization problems to minimize end-to-end outage probability, subject to UAV's power profile and trajectory for a DF relay scheme, respectively. The KKT conditions have been used to obtain closed-form solutions for the two formulated problems. Numerical simulation results validate all the theoretical results. We demonstrate that the performance of our proposed unified EH scheme outperforms that of existing techniques in the literature. [ABSTRACT FROM AUTHOR]

Abstract An increment in the number of User Equipment (UE) demands network replanning or introducing incipient devices which can provide dynamic support to the subsisting networks. One of these devices can be the Unmanned Aerial Vehicles (UAVs). However, being prodigiously dynamic and autonomous in some scenarios, these vehicles require an efficient mechanism for their deployment in currently operating wireless networks. In this paper, an efficient approach is proposed which utilizes the properties of the self-healing neural model and the concept of matrix-coloring in order to maximize the UAVs positioning likelihood for optimized throughput coverage and maximum UE to UAV mapping. The efficacy of the proposed approach is demonstrated in terms of amelioration in the throughput coverage and mapping of the UAV to subdivisions at low consumption of energy and memory by using numerical simulations. [ABSTRACT FROM AUTHOR]

The paradigm of previous 4G cellular technology has led to an increase in requirements for high data rate demands of mobile users in 5G. In order to meet this demand, constant densification of communication networks was required. Ultra Dense Networks (UDNs) have been proposed as a promising 5G technology to fulfill these requirements by the efficient and dynamic distribution of the radio resources. However, for implementation of UDN, mobile operators have to face many challenges such as severe interference resulting in a limited capacity due to the dense deployment of small cells, site location and acquisition for the deployment of base stations, backhauling issues, energy consumption, etc. In this article, in order to alleviate these limitations, we propose a novel idea of Ultra Dense Cloud-Drone Network (UDCDN) architecture. This scheme is featured with "on-demand" quality and substantial flexibility in terms of deployment. This anchors the challenges of traditional UDN settings and offers numerous benefits. Through simulation results of cell coverage, we verify the genuineness of implementing the proposed scheme and offer a new paradigm shift for UDN. [ABSTRACT FROM AUTHOR]

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search. [ABSTRACT FROM AUTHOR]