Source-linked AI summary
Energy Trade-off in Ground-to-UAV Communication via Trajectory Design
Dingcheng Yang, Qingqing Wu, Yong Zeng, Rui Zhang
TL;DR
The paper studies how a UAV collecting data from a fixed GT can balance reduced GT transmission energy against increased UAV propulsion energy. It derives energy models and Pareto-optimal transmit powers and trajectories for circular and straight flight, with numerical results corroborating the trade-off.
Problem
Flying closer to the GT can reduce its uplink transmission energy but may increase UAV propulsion energy, creating a G2U energy trade-off.
Method
The paper models GT communication and UAV propulsion energy, then optimizes GT transmit power and UAV trajectory for circular and straight flight.
Results
The study characterizes Pareto-optimal GT–UAV energy trade-offs for both trajectory types and provides numerical results corroborating them.
Takeaways & Limitations
GT transmission-energy savings should be evaluated jointly with UAV propulsion energy when designing G2U communication trajectories.
Abstract
from arXiv · showhide
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 UAV-enabled 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.
I. INTRODUCTION
The paper identifies a G2U energy trade-off: bringing the UAV closer reduces GT transmission energy but generally increases UAV propulsion energy. It characterizes this trade-off for circular and straight trajectories by jointly considering GT communication power and UAV propulsion power.
- UAV trajectory design adds mobility as a degree of freedom for optimizing wireless communication systems.
- Existing energy studies primarily minimize communication energy, while UAV propulsion energy is also important because it maintains flight and mobility.
- The study considers a point-to-point G2U data-collection system and accounts for both GT communication power and UAV propulsion power.
- Flying closer to the GT reduces its uplink transmission energy but generally requires more UAV propulsion energy, creating a fundamental G2U energy trade-off.
- For circular and straight flight, the paper derives optimal GT transmit powers and UAV trajectories achieving different Pareto-optimal energy trade-offs.
II. SYSTEM MODEL
The system models a fixed-altitude UAV collecting a target file from a fixed GT over a mission horizon. GT energy and UAV propulsion energy are coupled through mission time, transmit power, and trajectory.
- The UAV flies at constant altitude H while its ground-projected trajectory q(t) determines the time-varying GT–UAV distance.
- The GT uploads a file throughout mission time T using constant transmit power p1 constrained by its maximum allowable power.
- The uploaded information depends jointly on mission time T, GT transmit power p1, and UAV trajectory q(t), with the target condition Q̄(T, p1, q(t)) = Q.
- GT energy includes transmission and constant circuit power, whereas UAV energy is modeled mainly through propulsion power depending on its trajectory.
- The UAV’s communication-related energy is ignored because it is much smaller than propulsion energy, which is bounded by maximum propulsion power P̄2.
- For a given target throughput Q̄, the required GT and UAV energies are investigated as coupled functions of T, p1, and q(t).
III. GT-UAV ENERGY TRADE-OFF
The paper defines feasible GT–UAV energy pairs and studies their Pareto boundary for circular and steady straight flight. Pareto-optimal designs balance the two energy expenditures while meeting the target upload.
- The energy-consumption region C contains feasible pairs (E1, E2) sufficient to complete the target file upload.
- The Pareto boundary consists of energy pairs where reducing one energy requires increasing the other, with corresponding designs in (T, p1, q(t)).
- At a Pareto-optimal solution, the achieved throughput satisfies the target-upload requirement rather than exceeding it.
- Circular flight has the UAV orbit the GT, while steady straight flight connects specified initial and final locations at constant speed.
A. Circular Flight
Circular flight characterizes a GT-UAV energy trade-off through the trajectory radius and operation time. For fixed operation time, reducing UAV energy requires greater radius and increases GT energy, while Pareto-optimal pairs follow a closed-form boundary.
- Circular flight: Circular flight uses the UAV’s radius and flying speed to characterize the GT-UAV energy trade-off.The trajectory keeps the UAV at a fixed radius from the GT and derives the resulting energy expressions.
- Circular flight: The circular-flight speed does not affect GT energy consumption or communication throughput, so it is selected to minimize UAV propulsion power.For each radius, the optimal speed and corresponding minimum propulsion power are obtained from the propulsion model.
- Energy trade-off: The UAV cannot use a radius below a threshold imposed by the maximum propulsion-power constraint.The admissible operation time is likewise restricted by the propulsion-power constraint.
- Energy trade-off: For fixed operation time, decreasing UAV energy requires increasing the circle radius, which increases GT transmit power and GT energy.This establishes the energy trade-off for any given operation time.
- Pareto boundary: The two extreme points separately minimize UAV or GT energy; GT-energy minimization uses the minimum radius, whereas UAV-energy minimization sets GT power to its maximum.The corresponding optimization problems reduce to bisection or one-dimensional searches.
- Pareto boundary: Any circular-flight Pareto-optimal energy pair satisfies a closed-form expression parameterized by operation time, with GT energy monotonically decreasing in UAV energy.For a specified UAV-energy range, the GT energy is obtained by optimizing operation time under the feasibility constraints.
B. Straight Flight
Straight flight characterizes the GT-UAV energy trade-off by choosing a constant UAV speed along a path between fixed endpoints. Because the speed minimizing GT energy generally differs from that minimizing UAV energy, the Pareto solution balances both energy consumptions.
- Straight flight: Straight flight moves the UAV at constant speed from an initial location qA to a final location qB.The flying direction is represented by a unit-norm vector from qA to qB.
- Straight flight: With straight flight, throughput becomes a bivariate function of UAV speed and GT transmit power, while UAV energy becomes a univariate function of speed.The propulsion power for constant-speed flight is expressed using the aircraft parameters c1 and c2.
- Pareto boundary: The straight-flight Pareto boundary is derived from the energy and throughput equations, including propulsion-power feasibility constraints.The analysis first obtains the extreme points minimizing either GT or UAV energy, then characterizes the complete boundary.
- Energy trade-off: The speed minimizing UAV energy is unique, whereas the speed minimizing GT energy is generally different because GT transmit power increases with speed.The speed must therefore be selected to balance UAV and GT energy consumption.
- Pareto boundary: For a given UAV energy, two speeds may produce the same UAV energy, and the Pareto solution selects the smaller of the resulting GT energy values.The candidate speeds and GT energies are computed in closed form after solving for transmit power.
IV. NUMERICAL RESULTS
Numerical results verify the GT-UAV energy trade-off for circular and straight trajectories. The Pareto-optimal straight-flight speed depends on whether circuit or transmission energy dominates GT consumption.
- Circular flight: For circular flight, increasing UAV energy consumption E2 decreases GT energy consumption E1, with a stronger trade-off at smaller circuit power Pc.At Pc = 10mW, increasing UAV energy from 18 KJ to 40 KJ substantially reduces GT energy consumption.
- Straight flight: For straight flight, two different UAV speeds can achieve the Pareto boundary under different data transmission requirements.The comparison uses Q̄ = 30 Mb and 100 Mb, with V1 and V2 denoting speeds yielding the same UAV energy consumption.
- Straight flight: For Q̄ = 30 Mb, the larger speed V1 is Pareto-optimal because it reduces UAV traveling and file upload time, saving GT circuit energy.In this setup, the GT circuit power Pc = 50 mW is comparable to its transmit power.
- Straight flight: For Q̄ = 100 Mb, the lower speed V2 is Pareto-optimal because longer transmission time lowers GT transmit power when transmission energy dominates.The GT transmit power is much larger than its circuit power in this case.
V. CONCLUSIONS
The paper characterizes the fundamental energy trade-off between a UAV’s propulsion energy and its served GT’s communication energy. It derives this trade-off and corresponding designs for circular and straight flight, providing guidance for energy-efficient UAV communications.
- The paper investigates a fundamental energy trade-off between the UAV and its served GT in a G2U wireless communication system.
- It derives UAV propulsion and GT communication energy consumption under circular and straight trajectories.
- The analysis characterizes Pareto-optimal trade-offs together with optimal GT transmit power and UAV trajectory designs.
- The results are intended to shed light on designing energy-efficient UAV communications.