Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their wide coverage and high mobility. In this paper, we study a UAVenabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminals (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy trade-off in Ground-to-UAV (G2U) wireless communication. To characterize this trade-off, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT, and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal trade-off between them. Numerical results are provided to corroborate our study.

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Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle enabled mobile relaying system, where a high-mobility UAV is exploited to assist delivering the confidential information from a ground source to a legitimate ground destination with the direct link blocked, in the presence of a potential eavesdropper. We aim to maximize SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a finite time horizon. The formulated problem is a mixed-integer non-convex optimization problem that is challenging to be solved optimally. To make the problem tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes the subproblems. In addition, two special cases are considered as benchmarks in this paper. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmarks. Index TermsUAV communication, physical layer security, mobile relaying, secrecy energy efficiency, trajectory design. Recently, due to the high mobility, on-demand deployment/placement and line-of-sight (LoS) link, unmanned aerial vehicle (UAV) has attracted significant research interests in wireless communications [1], such as for traffic offloading, aerial BSs, mobile relaying [2]-[6], information broadcasting and data collection [7]-[10]. Compared to the traditional terrestrial communications, UAV-enabled communications have more flexible mobility and potentially reduced cost. For one thing, UAV-enabled communication systems is especially suitable to be applied for on-demand coverage or unexpected events due to the swift and flexible deployment of UAV. For another, there is more likely to have line-of-sight (LoS) link between UAV-ground link, which can significantly improve link capacity. In addition, UAV-enabled communications provide a new degree of freedom for performance enhancement by trajectory design. Generally speaking, UAV-enabled communications can best suit the communication requirement by trajectory optimization, where the UAV is subject to practical mobility constraints, such as initial/final locations, maximum speed, and maximum acceleration. However, the limited on-board energy of a UAV is one of the biggest challenges in UAV-enabled communications since a UAV needs much additional propulsion energy to maintain aloft. As a result, the authors in [11] obtained the analytical UAVs energy consumption model for fixed-wing UAVAs, which was expressed as a function with respect to UAVs speed and acceleration. Based on this, the work [6] studied the spectrum and energy efficiency maximization issues in a UAV-enable communication system, in which the UAV trajectory and transmit power are jointly optimized. In particular, the UAVs trajectory is needed to be optimized to achieve a high-rate communication with the ground nodes, while the energy consumption of the UAV is expected to be lower as much as possible [12]. One particula...

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In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless-powered communication network (WPCN), where a rotary-wing UAV is employed as a hybrid access point (AP) to serve multiple ground users (GUs). Specifically, the GUs harvest radio frequency (RF) energy from the signal sent by the UAV, which is then used by the GUs to power their uplink information transmission to the UAV. In practice, the mission completion time and energy consumption are two important indexes to evaluate the performance of UAV-enabled communication. To complete the mission as soon as possible, the UAV should fly above the ground users it serves at maximum speed, but this leads to more propulsion energy being consumed. Our objective is to reveal the energy-time tradeoff, characterized by the boundary of the so-called ''Energy-Time'' region. We first derive the mathematical form of the tradeoff, the UAV trajectory, user scheduling and mission completion time, as well as the time allocation, all of which need to be jointly optimized. To this end, we propose two communication protocols: (i) fly-hover-communicate and (ii) path discretization. For each protocol, we first find the two extremes, where minimum energy consumption and minimum mission completion time are achieved. We then complete the boundary for minimizing the energy consumption for given mission completion time. Moreover, because of the nonconvexity of the problem, we propose an algorithm to obtain a locally optimal solution based on the successive convex approximation (SCA) technique for both designs. Finally, the simulation results are provided to validate the effectiveness of our study. INDEX TERMS UAV communication, rotary-wing UAV, energy consumption, mission completion time, tradeoff, wireless powered communication networks.

Cover song identification is an important problem in the field of Music Information Retrieval. Most existing methods rely on hand-crafted features and sequence alignment methods, and further breakthrough is hard to achieve. In this paper, Convolutional Neural Networks (CNNs) are used for representation learning toward this task. We show that they could be naturally adapted to deal with key transposition in cover songs. Additionally, Temporal Pyramid Pooling is utilized to extract information on different scales and transform songs with different lengths into fixed-dimensional representations. Furthermore, a training scheme is designed to enhance the robustness of our model. Extensive experiments demonstrate that combined with these techniques, our approach is robust against musical variations existing in cover songs and outperforms state-of-the-art methods on several datasets with low time complexity.

In construction megaprojects, contractors and other participating entities sometimes go beyond what is stipulated in their contract and take initiatives that are irrational in pursuit of short-term economic benefit. This type of citizenship behavior is often observed in many construction megaprojects. However it has not been widely researched, and little is known about organizational citizenship behavior (OCB) and its characteristics in this field. This paper presents an overview of participating entities' OCB practice in construction megaproject (MOCB) utilizing a quantitative cross-case study. Industrial and academic experts' interviews were conducted to verify the reliability of the study results. As a result, a framework of MOCB was proposed and consisted of five behavior types in construction megaprojects, namely compliance, contingent collaboration, harmonious "guanxi" maintenance, conscientiousness, and initiative. Influenced by major contextual factors in construction megaprojects, MOCB