Graph Diffusion-Based AeBS Deployment and Resource Allocation in RSMA-Enabled URLLC Low-Altitude Wireless Networks

2025/07/05 by Xudong Wang, Wang, Xudong, Lei Feng +13 · 1 citation
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Opportunistic and Delay-Tolerant Networks #Satellite Communication Systems

paper · pdf · doi:10.48550/arxiv.2507.04081

openalex publication_date 2025/07/05 · openalex created_date 2025/10/20 · openalex updated_date 2026/07/28

Abstract

As a key component of low-altitude wireless networks, aerial base stations (AeBSs) provide flexible and reliable wireless coverage to support 6G ultra-reliable and low-latency communication (URLLC) services. However, limited spectrum resources and severe co-channel interference pose significant challenges to the deployment and resource allocation of AeBSs. To address these limitations, this paper proposes a novel rate-splitting multiple access (RSMA)-enabled transmission design to manage interference and enhance URLLC services in spectrum-constrained multi-AeBS networks. We formulate a joint optimization problem involving AeBS deployment, user association, and resource allocation to maximize the sum rate and coverage of system. Given the NP-hard nature of the problem, we propose a novel alternating optimization framework based on the generative graph diffusion models. Specifically, we model AeBSs and ground users as graph nodes, then we employ a discrete graph generation process solved via denoising diffusion to explore the combinatorial space of deployment and association strategies. Moreover, the successive convex approximation (SCA) is adopted to optimize AeBS beamforming and RSMA rate allocation under finite blocklength constraints. Extensive simulations demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, sum rate, and coverage, while also exhibiting robust performance under varying network densities and interference levels.

Citations

• Co-Design of Sensing, Communications, and Control for Low-Altitude Wireless Networks
• Generative AI Enabled Matching for 6G Multiple Access
• Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
• Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
• Graph Diffusion Policy Optimization
• URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
• Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
• A Survey on Generative Diffusion Model
• A Survey on Generative Diffusion Models
• On the Downlink Performance of RSMA-based UAV Communications
• Proximal Policy Optimization Algorithms
• Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks
• Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks
• Reinforcement Learning: An Introduction

Cited by

• When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks
• Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion

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