Optimization of UAV Heading for the Ground-to-Air Uplink (1107.0078v3)

Abstract: In this paper we consider a collection of single-antenna ground nodes communicating with a multi-antenna unmanned aerial vehicle (UAV) over a multiple-access ground-to-air wireless communications link. The UAV uses beamforming to mitigate the inter-user interference and achieve spatial division multiple access (SDMA). First, we consider a simple scenario with two static ground nodes and analytically investigate the effect of the UAV heading on the system sum rate. We then study a more general setting with multiple mobile ground-based terminals, and develop an algorithm for dynamically adjusting the UAV heading in order to maximize a lower bound on the ergodic sum rate of the uplink channel, using a Kalman filter to track the positions of the mobile ground nodes. Fairness among the users can be guaranteed through weighting the bound for each user's ergodic rate with a factor inversely proportional to their average data rate. For the common scenario where a high $K$-factor channel exists between the ground nodes and UAV, we use an asymptotic analysis to find simplified versions of the algorithm for low and high SNR. We present simulation results that demonstrate the benefits of adapting the UAV heading in order to optimize the uplink communications performance. The simulation results also show that the simplified algorithms perform near-optimal performance.

• The paper introduces an adaptive heading optimization algorithm leveraging Kalman filter predictions to enhance the sum data rate in multi-user ground-to-air uplinks.
• It analytically evaluates a static two-node scenario, showing that larger UAV antenna arrays reduce sensitivity to heading changes.
• Simulation results demonstrate that SDMA with real-time heading adjustments achieves near-optimal performance with significantly reduced computational complexity.

Optimization of UAV Heading for the Ground-to-Air Uplink

The paper by Jiang and Swindlehurst investigates the communication challenges faced by UAVs acting as relay nodes for ground-based communication networks. Their work focuses on optimizing the directional orientation (heading) of a multi-antenna UAV to enhance the performance of a multiple-access uplink communications system from multiple single-antenna ground nodes.

Problem Statement and Approach

In the context of UAV relay networks, interference is a common challenge, especially when handling multiple communication signals simultaneously. The UAV employs beamforming techniques to perform spatial division multiple access (SDMA), mitigating the interference from multiple signals received from different users. The main goal is to dynamically adjust the UAV's heading to optimize the sum data rate of the communications link.

The authors tackle this by first evaluating a basic two-node static scenario, where they analytically assess the UAV's heading influence on the system's sum rate. For the more complex case involving multiple mobile ground nodes, they propose an algorithm for optimizing UAV heading via a Kalman filter-based prediction of the ground nodes' positions. This prediction helps in optimizing a lower bound on the ergodic sum rate of the wireless uplink. Additionally, fairness among users is ensured by weighting each user's rate based on their average data throughput.

Key Contributions and Findings

1. Analytical Evaluation for Static Two-Node Scenario: For a simplified scenario with two static ground nodes, a rectangular path optimization model is considered. The solution indicates that increasing the array size on the UAV minimizes the sensitivity of the system's performance to heading changes.
2. Adaptation for Mobile Networks: A comprehensive adaptive heading adjustment algorithm for mobile networks is proposed, which utilizes position predictions from a Kalman filter. This step-wise optimization shows the UAV adjusting its heading in real time to optimize the link's ergodic sum rate.
3. SinR Analysis and Performance in High K-Factor Channels: For systems characterized by high Rician K-factor channels, approximations are derived for both low and high SNR conditions. These approximations lead to simplified solutions which approach near-optimal performance, confirmed via simulation.

Simulation and Results

The simulation results substantiate the efficacy of the proposed algorithms. Specifically, the simulation tests demonstrate:

• The substantial performance benefits of SDMA over TDMA, indicating that simultaneous channel use with spatial separation provides a notable throughput gain.
• The UAV's trajectory is adaptively controlled to closely track the movement and distribution of mobile ground nodes, even amidst changes in user positions and velocities.
• The asymptotic solutions offer a near-optimal heading strategy with significantly reduced computational complexity, validating the line-search method used in more complex scenarios.

Implications and Future Outlook

This research underscores the potential for UAVs to enhance communication network performance through strategic heading control. It aligns with the broader trend of utilizing UAVs for dynamic network operations, facilitating enhanced data throughput and user fairness. Future developments could explore scenarios with more complex node dynamics or environmental factors, refine the Kalman filter parameters for better predictive accuracy, and potentially incorporate machine learning techniques to adaptively refine operation without explicit path models.

Jiang and Swindlehurst's methodology establishes a robust framework for optimizing UAV heading in ground-to-air networks, offering a promising avenue for improving communication reliability and efficiency in ad hoc network environments and emergency response scenarios. The integration of more diverse communication strategies alongside adaptive heading control presents a fertile ground for ongoing research in UAV-assisted network communication.