Energy Minimization for Wireless Communication with Rotary-Wing UAV (1804.02238v1)

Abstract: This paper studies unmanned aerial vehicle (UAV) enabled wireless communication, where a rotarywing UAV is dispatched to send/collect data to/from multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive an analytical propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN when hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem (TSP) and convex optimization techniques. Next, we consider the general case where the UAV communicates also when flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a locally optimal solution by applying the successive convex approximation (SCA) technique. Numerical results show the significant performance gains of the proposed designs over benchmark schemes, in achieving energy-efficient communication with rotary-wing UAVs.

• The paper develops a detailed propulsion power model and formulates a non-convex optimization problem to minimize energy consumption in UAV-enabled wireless communication.
• It introduces a fly-hover-communicate protocol and employs successive convex approximation to optimize UAV trajectories and scheduling with ground nodes.
• Simulation results validate that the proposed methods significantly reduce energy consumption by balancing the trade-off between flight dynamics and communication efficiency.

Energy Minimization for Wireless Communication with Rotary-Wing UAV

The paper discusses a comprehensive approach to minimize energy consumption in rotary-wing Unmanned Aerial Vehicle (UAV)-enabled wireless communication. The focus is on the combined reduction of propulsion energy and communication-related energy while meeting the communication throughput requirements for multiple ground nodes (GNs). The researchers develop and analyze a propulsion power consumption model tailored for rotary-wing UAVs and leverage this model to optimize UAV trajectories and communication time allocation in a non-convex optimization framework.

Analytical Models and Problem Formulation

The paper begins by deriving a detailed analytical model for the propulsion power consumption of rotary-wing UAVs. This model incorporates several elements including blade profile power, induced power, and parasite power, making it significantly more complex than the models used for fixed-wing UAVs. The power consumption model is expressed as:

$$P(V) = P_0 \left(1 + \frac{3 V^2}{U_{\text{tip}}^2} \right) + P_i \left( \sqrt{1 + \frac{V^4}{4 v_0^4} - \frac{V^2}{2v_0^2}} \right)^{1/2} + \frac{1/2} d_0 \rho s A V^3,$$

where $P_0$ and $P_i$ are constants related to the blade profile and induced power, respectively, $U_{\text{tip}}$ is the rotor blade tip speed, and $v_0$ is the mean rotor induced velocity in hover.

Using this model, the researchers formulate an optimization problem that aims to minimize total UAV energy consumption. This problem involves variables including UAV trajectory, communication time allocation among GNs, and total mission completion time. The problem is non-convex and involves continuous variables over time, which complicates finding an optimal solution.

Fly-Hover-Communicate Protocol

To gain initial insights, the authors consider a simplified protocol called "fly-hover-communicate." Here, the UAV successively visits a series of hovering locations and communicates with one GN at each location. To solve this, the researchers propose an efficient algorithm leveraging the Travelling Salesman Problem (TSP) and convex optimization techniques. Specifically, they decompose the problem into optimizing hovering locations, durations, and the flight path connecting these locations.

Path Discretization and Successive Convex Approximation

For the general case where the UAV communicates while flying, the authors introduce a novel path discretization method. This transforms the continuous optimization problem into a discrete one by representing the UAV path as a sequence of waypoints. By doing so, the problem becomes more tractable, with a finite number of optimization variables. The researchers solve this discretized problem using the Successive Convex Approximation (SCA) technique, which ensures a local optimum by iteratively solving a series of convex approximations of the original non-convex problem.

Numerical Results

Simulation results demonstrate the significant energy savings achieved by the proposed designs compared to benchmark schemes. The proposed methods show a substantive reduction in energy consumption using optimized UAV trajectories and efficient communication scheduling. Key results include:

• With the fly-hover-communicate protocol, the UAV's optimized path and hovering strategy balance the trade-off between traveling and communication energy.
• The proposed SCA-based algorithm further reduces energy consumption by allowing the UAV to communicate while in motion, showing notable improvements over simplistic hovering-based approaches.
• The researchers validate the robustness of their models and methods across various throughput requirements.

Implications and Future Work

This research has significant practical implications for the deployment of rotary-wing UAVs in real-world communication scenarios such as disaster recovery, IoT data collection, and temporary coverage enhancement. Theoretical advancements in trajectory optimization and energy-efficient communication protocols can be extended to other UAV types and applications.

Future developments might explore adaptive algorithms that dynamically adjust UAV flight paths based on real-time data and environmental changes. There is also potential for integrating machine learning techniques to predict optimal paths and communication schedules. Such enhancements could further improve the energy efficiency and operational range of UAV-enabled communication systems.

Conclusion

This paper presents a well-structured and mathematically rigorous approach to reducing energy consumption in rotary-wing UAV-enabled wireless communication. Through a combination of analytical modeling, algorithmic innovation, and numerical validation, the research addresses a crucial aspect of UAV operations, paving the way for more sustainable and efficient UAV communication systems.