Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

By Dingcheng Yang, Qingqing Wu, Yong Zeng, Rui Zhang. IEEE Transactions on Vehicular Technology, 2017

381 citations recorded by the source corpus.

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (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 tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, 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 tradeoffs between them. Numerical results are provided to corroborate our study.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e442e48ff6f09b6a9db00f3715f65a993f66ca93

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

1. Rui Zhang
2. Qingqing Wu
3. Robert Schober
4. Yuanwei Liu
5. Yong Zeng
6. Shuowen Zhang
7. Changsheng You
8. Jie Xu

In Electrical and Electronic Engineering

1. Rui Zhang
2. Robert Schober
3. Qingqing Wu
4. Yuanwei Liu
5. Changsheng You
6. Yong Zeng
7. Shuowen Zhang
8. Jie Xu

Citations in the corpus

• Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks (2017, background): “Although a single UAV has demonstrated its advantages in performance enhancement for wireless networks [1], [16], [23], [25]–[29], it has limited capability in general and may not guarantee availability during the entire mission due to its practical size, weight and power (SWAP) constraints [9].”
• Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, background): “Based on the energy model in [26], the authors in [32] further revealed an interesting trade-off between UAV’s energy consumption and that of the GNs it communicating with.”
• Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “[237] Single UAV Outdoor Directional 2 GHz Collecting data using Two practical”
• Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “tal mobile energy consumption, including the computation, communication, as well as flying energy consumption. Moreover, energy consumption at the ground users served by the UAV was considered in [77]–[79]. In [77], the placement and movement of multiple UAVs collecting data from multiple IoT devices”
• UAV-Assisted Emergency Networks in Disasters (2019, methodology): “in [6] via two kinds of trajectory optimization.”
• Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, background): “Thirdly, UAVs can also be used as aerial base stations to collect data from Internet of Things (IoT) devices on the ground, where building a complete cellular infrastructure is unaffordable [7, 10].”
• A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “This thus leads to an energy consumption tradeoff between UAVs and ground devices [25].”
• UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells (2018, neutral): “via trajectory optimization in [15].”
• Common Throughput Maximization in UAV-Enabled OFDMA Systems With Delay Consideration (2018, background): “Finally, as the UAV’s energy consumption and hence endurance are crucial in practice, it is appealing to consider energy-efficient UAV trajectory design [16], [37] under heterogenous user delay requirements.”
• Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “[174] D.”
• Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (2019, methodology): “[29], hence the channel between the UAV and TDs and that between the UAV and AP are modeled by the free space path loss model.”
• UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, background): “In [20], UAV trajectory and ground terminal transmit power are jointly optimized for both circular and straight trajectories to reveal a fundamental trade-off between the UAV propulsion energy consumption and ground terminal communication energy consumption.”
• AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks (2020, background): “The channel power gain at the reference distance 1 m is set as κ 0 = − 50 dB and the noise power σ 2 w = − 110 dBm [45].”
• UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “Furthermore, the authors in [39] presented a UAV enabled data gathering system with the aim to study the energy trade-off problem in the UAV-toground communications.”
• 3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting (2019, background): “Such advantages have attracted growing research attention in recent years on UAV-enabled WSNs, including the designs of UAV trajectory, SN wakeup schedule, trajectory-aware signal modulation and coding, etc [4]–[12].”