Energy-Efficient UAV Communication With Trajectory Optimization

By Yong Zeng, Rui Zhang. IEEE Transactions on Wireless Communications, 2016

1,897 citations recorded by the source corpus.

Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, assuming that the UAV flies horizontally with a fixed altitude, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV’s trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV’s energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV’s flying speed, direction, and acceleration. Based on the derived model and by ignoring the radiation and signal processing energy consumption, the energy efficiency of UAV communication is defined as the total information bits communicated normalized by the UAV propulsion energy consumed for a finite time horizon. For the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a simple circular UAV trajectory, under which the UAV’s flight radius and speed are jointly optimized to maximize the energy efficiency. Furthermore, an efficient design is proposed for maximizing the UAV’s energy efficiency with general constraints on the trajectory, including its initial/final locations and velocities, as well as minimum/maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/774c755a1c2bda4e312547c64fe1531c1f3ce3b4

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. Yong Zeng
4. Merouane Debbah
5. Shuowen Zhang
6. Medhi Bennis
7. Yuanwei Liu
8. Walid Saad

In Computer Networks and Communications

1. Rui Zhang
2. Yong Zeng
3. Merouane Debbah
4. Medhi Bennis
5. Qingqing Wu
6. Walid Saad
7. Lajos Hanzo
8. Jihong Park

Citations in the corpus

• Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, background): “Note that the propulsion power consumed by the UAV when it changes its direction is negligible [23].”
• UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, background): “In the context of energy efficiency, significant work has been devoted to minimize the UAV energy consumption, such as [10], or sensor network energy to prolong the network lifetime such as [11].”
• IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design (2022, background): “Specifically, thanks to high maneuverability and cost-effective deployment, UAVs are gaining popularity in serving as aerial communication platforms to enhance the coverage, capacity and energy efficiency of existing wireless networks [3]–[5].”
• Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence (2018, background): “Moreover, while power consumption in terrestrial communication systems is mainly related to communication functions, the majority of power consumption in the aerial segments is to maintain the aloft and support mobility [9].”
• Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, methodology): “The propulsion power and maximal battery power for each UAV are respectively set as Qj = 100 W [20] and P uav j,max = 110 W.”
• 3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage (2017, methodology): “The available energy is consumed in the onboard BS and in powering the UAV platform [5].”
• UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “Similar to [16], the wireless channels between the UAV and the AP as well as the UEs are assumed to be dominated by LoS links, which is verified by recent field experiments done by Qualcomm [24].”
• Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, methodology): “An analytical framework for trajectory optimization of a fixed-wing UAV for energy-efficient communications was presented in [14].”
• Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018, background): “At the same time, due to the limited battery capacity, UAVs need to maximize their energy efficiency, which is difficult to be optimized per se even for a single UAV under a given constant wind velocity [7].”
• What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G (2021, background): “As for the communication power—amounting to a few watts in most scenarios—, it is negligible when compared to the hundreds of watts required for propulsion [86].”
• Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks (2018, methodology): “The selection of the simulation parameters are based on the existing works and 3GPP specifications [15], [33].”
• Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, methodology): “In [17], the new design paradigm of jointly optimizing the communication throughput and the UAV’s energy consumption was conceived for the determining trajectory of UAV, including its initial/final locations and velocities, as well as its minimum/maximum speed and acceleration.”
• 3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements (2017, background): “Furthermore, the energy available for powering the onboard electronics is limited because of using batteries as a source of power [4].”
• Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “[27] Lemma 2: Using the SCA method, the following inequality can be obtained,”
• Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “Such new problems need accurate energy consumption models for different types of UAV [23], [24] and are expected to reveal interesting new trade-offs between the communication performance and energy consumption in the UAV trajectory design.”
• UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells (2018, methodology): “to first utilize successive convex optimization to solve the problem effectively [16] and [17].”
• Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, methodology): “Zhang et al. [8] have developed an offloading strategy to reduce the energy consumption under the delay constraints.”