[Paper Review] UAV-Enabled Data Collection for Wireless Sensor Networks with Distributed Beamforming

Tianxin Feng, Lifeng Xie+2 more|arXiv (Cornell University)|Apr 23, 2020

UAV Applications and Optimization43 references4 citations

TL;DR

This paper proposes a joint UAV trajectory and power allocation design for UAV-enabled wireless sensor networks using distributed beamforming to maximize data-rate throughput in delay-tolerant scenarios and minimize outage probability in delay-sensitive scenarios. By leveraging convex optimization and approximation techniques, the proposed scheme significantly outperforms benchmarks and approaches the theoretical performance upper bound as mission duration increases.

ABSTRACT

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV's trajectory design and the GNs' transmit power allocation over time, subject to the UAV's flight speed constraints and the GNs' individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV's flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV's flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV's flight speed constraints are ignored.

Motivation & Objective

• To enhance data collection efficiency in wireless sensor networks using UAVs as mobile fusion centers.
• To address the challenge of optimizing UAV trajectory and ground node power allocation under practical constraints.
• To maximize average data-rate throughput in delay-tolerant applications with adaptive-rate transmission.
• To minimize transmission outage probability in delay-sensitive applications with fixed-rate transmission.
• To develop efficient algorithms for non-convex optimization problems arising from joint trajectory and power design.

Proposed method

• Formulates two non-convex optimization problems: one for throughput maximization and one for outage minimization under UAV speed and node power constraints.
• Solves relaxed problems ignoring UAV speed constraints to derive optimal solutions that reveal performance upper bounds.
• Identifies that optimal trajectories for both problems exhibit a multi-location-hovering structure, differing only in power allocation strategies.
• Applies convex optimization and approximation techniques to design sub-optimal solutions for the general problem with realistic UAV speed constraints.
• Uses iterative algorithms based on successive convex approximation (SCA) to handle non-convexity and converge to high-quality solutions.
• Employs a centralized design framework where the UAV coordinates distributed beamforming via phase alignment and power control at ground nodes.

Experimental results

Research questions

• RQ1 What is the optimal UAV trajectory structure that maximizes data-rate throughput in a distributed beamforming-enabled WSN?
• RQ2 How does joint power allocation and trajectory design improve outage performance in delay-sensitive UAV data collection?
• RQ3 What performance gain is achievable by jointly optimizing UAV mobility and ground node power compared to separate optimization?
• RQ4 How close can practical designs approach the theoretical performance upper bound when UAV speed constraints are ignored?
• RQ5 What are the impacts of increasing mission duration and transmit power on the performance gap between proposed schemes and upper bounds?

Key findings

• The proposed joint trajectory and power allocation design significantly outperforms benchmark schemes in both throughput and outage probability metrics.
• The optimal trajectory for both scenarios exhibits a multi-location-hovering structure, indicating that hovering at key locations is critical for performance.
• When mission duration is sufficiently long, the proposed design approaches the theoretical performance upper bound derived from the relaxed problem without speed constraints.
• The performance gap between the proposed scheme and the upper bound increases with higher average transmit power, due to the trade-off between optimal hovering and path transmission.
• Power optimization plays a crucial role: trajectory-only designs suffer a sharp performance drop, highlighting the importance of joint power and trajectory control.
• In delay-sensitive scenarios, the proposed method achieves lower outage probability than all benchmarks, and approaches the upper bound as flight duration increases.