An unmanned aerial vehicle (UAV) can be utilized as a flying data collector and wireless power source in wireless charging sensor networks (WCSNs). Different from conventional studies that focused to maximize the efficiency of wireless power transfer (WPT) for UAV route design, in this paper we maximize the lifetime of WCSNs by considering sensor energy consumption and energy harvesting simultaneously. We consider simultaneous wireless information and power transfer (SWIFT), where data collection capability guarantees power leftover for UAV to complete its round-trip flight. Our objective is to jointly optimize the UAV hovering location and duration to maximize the minimum energy of sensors after data transmission and energy harvesting under data collection and UAV energy consumption constraints. To tackle this non-convex optimization problem, we first assume that UAV hovering location for each sensor is fixed and optimize UAV hovering duration by Lagrange multiplier method. Next, for each UAV hovering location, we propose a geometry-based update algorithm, which can be used to find initial feasible UAV routes to the problem. Lastly, a near-optimal UAV route is determined by adjusting the initial feasible UAV route iteratively, where UAV hovering locations and duration are updated at each iteration. Numerical results are provided to validate the performance of our proposed algorithm.

A novel resource allocation (RA) algorithm for non-orthogonal transmission is proposed to maximize overall throughput of user equipments (UEs) in unmanned aerial vehicle (UAV) relay systems (URSs), which jointly optimizes transmit duration and power of signals at base station (BS) and UAV. Also, the proposed RA algorithm is shown to maximize the overall throughput of UEs while guaranteeing a seamless UAV relay transmission through the analysis of outage probability and power control strategy. Simulation results reveal that the proposed RA algorithm for non-orthogonal transmission in URSs achieves the maximum throughput of UEs within the actual operation range of UAV.

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area.

Automobiles provide a convenient form of transportation, and the number of automobiles in the world has been increasing rapidly, from 193 million in 1970 to more than 796 million in recent years [1]. However, automobiles have created a number of serious problems, such as accidents, traffic congestion, and air pollution, among which traffic accidents are one of the most serious and urgent problems threatening the safety of automobile users.

This paper proposes an Adaptive Bi-directional Rapidly-exploring Random Tree (ABi-RRT) algorithm with the objective of addressing challenges in three-dimensional path planning of unmanned aerial vehicle (UAV) operating in complex environments. The algorithm utilizes an adaptive sampling strategy and an adjustable step size to enhance performance relative to the Bi-RRT algorithm. In contrast to the Bi-RRT algorithm, which utilizes a random sampling strategy and a fixed step size, potentially leading to inefficiencies and limited search effectiveness, the ABi-RRT algorithm incorporates a dynamic mechanism. This mechanism adjusts the sampling strategy and step size based on the complexity of the search environment. This adaptive approach reduces the randomness in sampling, improves the target-oriented nature of the search, and consequently enhances overall search efficiency. Additionally, a greedy path pruning algorithm is integrated to minimize the path length and reduce the number of points along the path. The utilization of cubic B-spline curves further improves the coherence and smoothness of the generated paths. Simulation results demonstrate that the ABi-RRT algorithm can rapidly and effectively produce paths that are smoother, shorter, and of superior quality compared to existing RRT algorithms. This performance is particularly valuable for UAVs operating under stringent performance constraints in complex three-dimensional environments.

UAV equipped three-dimensional (3D) wireless networks can provide a solution for the requirements of 5G communications, such as enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). Especially, the introduction of an unmanned aerial vehicle (UAV) as a relay node can improve the connectivity, extend the terrestrial base station (BS) coverage and enhance the throughput by taking advantage of a strong air-to-ground line of sight (LOS) channel. In this paper, we consider the deployment and resource allocation of UAV relay network (URN) to maximize the throughput of user equipment (UE) within a cell, while guaranteeing a reliable transmission to UE outside the coverage of BS. To this end, we formulate joint UAV deployment and resource allocation problems, whose analytical solutions can be hardly obtained, in general. We propose a fast and practical algorithm to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. The transmit power at BS and UAV is determined in advance based on the availability of channel state information (CSI). Simulation results demonstrate that the proposed algorithms can significantly reduce the computational effort and complexity to determine the optimal UAV location and transmit time slots over an exhaustive search.