Low-altitude Multi-UAV-assisted Data Collection and Semantic Forwarding for Post-Disaster Relief

2026-01-22

AI Summary

This work addresses the challenges of post-disaster communication, where long-range links between unmanned aerial vehicles (UAVs) and base stations are fragile, and massive data volumes coupled with limited onboard resources create severe transmission bottlenecks. To overcome these issues, the paper proposes a semantic-driven collaborative multi-UAV network architecture that integrates clustering, semantic information extraction, and cooperative beamforming. A multi-objective optimization framework is developed to jointly enhance user data rates and semantic transmission efficiency. The authors innovatively introduce a large language model-guided alternating optimization algorithm to effectively solve the resulting high-dimensional NP-hard problem. Experimental results demonstrate that the proposed scheme improves transmission rate and semantic rate by 26.8% and 22.9%, respectively, compared to conventional approaches, while significantly reducing energy consumption.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The low-altitude economy (LAE) is an emerging economic paradigm which fosters integrated development across multiple fields. As a pivotal component of the LAE, low-altitude uncrewed aerial vehicles (UAVs) can restore communication by serving as aerial relays between the post-disaster areas and remote base stations (BSs). However, conventional approaches face challenges from vulnerable long-distance links between the UAVs and remote BSs, and data bottlenecks arising from massive data volumes and limited onboard UAV resources. In this work, we investigate a low-altitude multi-UAV-assisted data collection and semantic forwarding network, in which multiple UAVs collect data from ground users, form clusters, perform intra-cluster data aggregation with semantic extraction, and then cooperate as virtual antenna array (VAAs) to transmit the extracted semantic information to a remote BS via collaborative beamforming (CB). We formulate a data collection and semantic forwarding multi-objective optimization problem (DCSFMOP) that jointly maximizes both the user and semantic transmission rates while minimizing UAV energy consumption. The formulated DCSFMOP is a mixed-integer nonlinear programming (MINLP) problem that is inherently NP-hard and characterized by dynamically varying decision variable dimensionality. To address these challenges, we propose a large language model-enabled alternating optimization approach (LLM-AOA), which effectively handles the complex search space and variable dimensionality by optimizing different subsets of decision variables through tailored optimization strategies. Simulation results demonstrate that LLM-AOA outperforms AOA by approximately 26.8\% and 22.9\% in transmission rate and semantic rate, respectively.

Problem

• low-altitude UAVs
• post-disaster communication
• data bottleneck
• long-distance links
• semantic forwarding

Innovation

• semantic communication
• multi-UAV collaboration
• virtual antenna array
• collaborative beamforming
• LLM-enabled optimization