This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms.


    Access

    Download


    Export, share and cite



    Title :

    Accuracy-Aware MLLM Task Offloading and Resource Allocation in UAV-Assisted Satellite Edge Computing


    Contributors:
    Huabing Yan (author) / Hualong Huang (author) / Zijia Zhao (author) / Zhi Wang (author) / Zitian Zhao (author)


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

    Hongxia Zhang / Shiyu Xi / Hongzhao Jiang et al. | DOAJ | 2023

    Free access


    V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks

    He, Junjin / Wang, Yujie / Du, Xin et al. | ArXiv | 2021

    Free access