Optimization and Deep Learning based Resource Allocation for UAV-Aided Wireless Communication with Rotatable Antenna Array

Jul 16, 2026

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Multi-antenna unmanned aerial vehicle (UAV)-aided wireless communication enhances capacity and service quality in future networks. The work equips a UAV with a rotatable antenna array (RAA) driven by a 3D gimbal to provide extra spatial degrees of freedom for multiuser transmission and interference control. A joint design of RAA orientation and beamforming maximizes multiuser sum-rate under per-user QoS constraints. A penalty dual decomposition optimization iteratively attains reliable QoS satisfaction, while a graph neural network deep learning framework reduces computation time and remains robust to user position errors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

Introduction

Motivation: limits of fixed antenna arrays

Proposed concept: rotatable antenna arrays

Related work on UAV communications with RAAs

Optimization and Deep Learning based Resource Allocation for UAV-Aided Wireless Communication with Rotatable Antenna Array Fengcheng Pei, Lin Xiang, Anja Klein, and Robert Schober arXiv :2607 .08420v 1 [ cs .IT] 9 Jul 2026 Abstract—Multi-antenna unmanned aerial vehicle (UAV)-aided communication presents a promising solution to increase the system capacity and improve the quality of service (QoS) of the future wireless networks. In this paper, we equip a UAV platform with a rotatable antenna array (RAA), which can be rotated flexibly in three-dimensional (3D) space via an onboard gimbal, enabling additional spatial degrees of freedom (DoFs) for improving multiuser signal transmission and interference management. Compared with a conventional fixed antenna array (FAA), the RAA can proactively align users with the high-gain region of its antenna elements and reduce the spatial channel correlations among users. To demonstrate the advantages of RAA, we jointly design the RAA orientation and beamforming to maximize the sum-rate of multiple users subject to per-user QoS constraints. The formulated problem is highly nonconvex and exhibits strong coupling between the RAA orientation and beamforming variables. To solve this challenging problem, we propose first an optimization framework based on the penalty dual decomposition (PDD) method to iteratively optimize RAA orientation and beamforming. While the optimization framework yields high reliability in QoS satisfaction and favorable sum-rate performance, its iterative nature may hinder real-time deployment. To accelerate the joint design and preserve a high-quality solution, we further propose a deep learning (DL) framework based on graph neural networks (GNNs). Simulation results demonstrate that RAAs significantly outperform FAAs in UAVaided communication. Additionally, the proposed optimization framework is capable of satisfying stringent QoS requirements with high reliability, while the proposed DL framework attains comparable sum-rate performance with substantially reduced computation time and exhibits robustness to user position information errors. Index Terms—Rotatable antenna array (RAA), unmanned aerial vehicle (UAV), optimization, graph neural network (GNN). I. INTRODUCTION Multi-antenna unmanned aerial vehicle (UAV)-aided wireless communication presents a promising solution to increase the spectral efficiency and enhance the service capabilities of sixth-generation (6G) cellular networks in both normal and emergency scenarios [1]–[3] . However, the elevated altitude of UAV platforms gives rise to line-of-sight (LoS) dominant channels for terrestrial users, which may create severe multiuser interference and hinder support for high user densities. Furthermore, UAV platforms are typically constrained by size, weight, and power (SWAP), restricting the dimensions of onboard antenna arrays, which further complicates multiuser interference management. To address these challenges, existing research has primarily investigated the joint design of beamforming and UAV trajectory [5], [6] to maximize, e.g., the sum-rate, where the orientations of the onboard antenna arrays are fixed. For example, the UAV in [5] is equipped with a vertically placed uniform linear array (ULA) throughout flight. Such fixed antenna arrays (FAAs) limit the ability to spatially separate users in the angular domain and may lead to highly correlated channels among users, significantly restricting the sum-rate performance. Moreover, during the flight of the UAV, users may fall within the low-gain regions of the FAA antenna elements, jeopardizing signal transmission and connectivity. To overcome the drawbacks of FAAs, rotatable antenna arrays (RAAs) have been proposed in [7] . Unlike FAAs, the orientation of RAAs can be adjusted and optimized flexibly in three-dimensional (3D) space, providing additional spatial degrees of freedom (DoFs) . Through orientation design, the RAAs can separate users in

FAQs

What problem does the rotatable antenna array (RAA) address in UAV-aided communication?

The RAA adds 3D controllability so users can be aligned to high-gain antenna regions and separated in the angular domain, mitigating multiuser interference and improving performance versus fixed antenna arrays.

How is the RAA orientation and beamforming design formulated?

The paper jointly optimizes RAA orientation and multiuser beamforming to maximize the sum-rate under per-user QoS constraints, resulting in a highly nonconvex problem with strong variable coupling.

What methods are proposed to solve the optimization and enable fast deployment?

An iterative penalty dual decomposition (PDD) framework first optimizes orientation and beamforming with reliable QoS satisfaction. A graph neural network (GNN) deep learning framework then accelerates the joint design while maintaining comparable sum-rate and robustness to user position information errors.

Jul 16, 2026

Upload by Aurelia