Joint Frame Structure, Beamwidth, and Power Allocation for UAV-Aided Localization and Communication

TL;DR

This paper introduces a joint optimization framework for UAV-assisted localization and communication, improving spectral efficiency by optimizing frame structure, beamwidth, and power allocation, with significant performance gains demonstrated.

Contribution

It presents a novel joint optimization approach for UAV-aided localization and communication, deriving bounds and proposing an efficient algorithm for maximizing spectral efficiency.

Findings

Achieves over 70% performance gain compared to non-localization benchmarks.

Proposed method approaches the theoretical upper bound with lower complexity.

Highlights Doppler effect as a dominant factor affecting spectral efficiency.

Abstract

In wireless sensors networks, integrating localization and communications techniques is crucial for efficient spectrum and hardware utilization. In this paper, we present a novel framework of unmanned aerial vehicle (UAV)-aided localization and communication for ground node (GN), where the average spectral efficiency (SE) is used to reveal the intricate relationship among frame structure, channel estimation error, and localization accuracy. In particular, we first derive the lower bounds for channel estimation error and the three dimensional location prediction error. Leveraging these comprehensive analysis, we formulate a problem to maximize the average SE in UAV-GN communication, where the frame structure, beamwidth and power allocation are jointly optimized. Subsequently, we propose an efficient iterative algorithm to address this non-convex problem with closed-form expressions for beamwidth and power allocation. Numerical results demonstrate that the performance of our proposed method can approach the upper bound with much lower complexity, and achieve over 70\% performance gain compared to non-localization benchmarks. Additionally, the analysis highlights the dominant impacts from the Doppler effect over noise on the average SE.