Throughput Maximization for UAV-Enabled Mobile Relaying Systems

By Yong Zeng, Rui Zhang, Teng Joon Lim. IEEE Transactions on Communications, 2016

1,194 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ec8f7daad905e4e553d3299f989b6e1c97382bd0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, methodology): “In [21], the authors maximized the throughput by optimizing the UAV trajectory jointly with the transmitted power for a mobile relay node mounted on a UAV, subject to the UAV mobility constraints.”
Securing UAV Communications via Joint Trajectory and Power Control (2018, background): “, [11]–[14], [16], [41], and [42]) on trajectory optimization for various UAV communication systems, which consider different system setups and design objectives.”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, background): “e threedimensional (3D) space for maximization of the number of served users and the coverage area was studied in [8] and [9], respectively. However, the UAV-based communication systems considered in [5]–[9] were powered by on-board batteries with limited energy storage capacity, leading to a constr”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, methodology): “Thus, the energy efficiency is an important performance metric to be evaluated and optimized, especially for high-mobility UAVs [93], [154].”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “On the other hand, the emerging 3D wireless networks also introduce new design opportunities to better serve users in a cost-effective manner [19]–[22].”
3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting (2019, neutral): “Besides UAV-enabled WSNs, UAV trajectory design has been widely investigated in other wireless networks, such as UAV-assisted terrestrial communication [13]–[16], relay systems [17], cellular networks [18], radio access networks [19], and wireless power transfer networks [20].”
Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network (2017, background): “It has been shown that short-distance lineof-sight (LoS) communication links between UAV and ground terminals can be efficiently exploited in various UAV-enabled wireless networks for performance enhancement by properly designing the UAV’s trajectory [3], [4].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “The trajectory of UAVs has to be thoroughly studied in order to satisfy their mission requirements and at the same time fulfilling the optimum throughput for the ground terrestrial networks and GUs [196].”
Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, methodology): “To this end, prior works such as [24] and [26] mostly adopted the method of time discretization, where the time horizon [0, Tt] is discretized into a finite number of time slots with sufficiently small slot length δt.”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “In [17], the UAV-enabled mobile relaying systems were studied, where the throughput was maximized by optimizing the transmit power allocation and the UAV’s trajectory.”
UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization (2017, methodology): “Note that the SCP-based trajectory design has been studied for throughput or energy efficiency maximization for UAV-enabled wireless communication systems [18], [19], but the results cannot be directly applied for UAV-enabled WPT systems considered in this paper.”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, background): “, flying computing cloudlets [18],2 UAV-aided data collection [19], [20], and mobile relays [21].”
Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, methodology): “[14] proposed an iterative algorithm to optimize the UAV’s trajectory and power allocation.”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, positive): “Specifically, the authors in [212] proposed a newmobile relaying technique in which relay node is mounted on a UAV capable of moving at high speeds.”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “As an extension to [50], a UAV-mounted mobile amplify-and-forward (AF) relay was considered in [51], where the UAV trajectory and transmit power were jointly optimized to minimize the outage probability of the relay network.”
Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity (2017, methodology): “In [14] the trajoctory of a mobile UAV relaying is optimized for maximum throughput.”
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning (2016, methodology): “In particular, the problem of jointly optimizing the power allocation at source and moving relay, as well as the relay’s trajectory, is tackled in [8] with the aim of maximizing the throughput under mobility constraints on the relay’s speed and terminal locations and assuming a decodestore-and-forwa”