A Task Prediction-Augmented Hierarchical Offloading Method for Space-Air-Ground Integrated Networks

ZHANG Linghao; XU Bo; SUN Jinlong; LAI Haiguang; ZHAO Haitao

doi:10.11999/JEIT260217

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ZHANG Linghao, XU Bo, SUN Jinlong, LAI Haiguang, ZHAO Haitao. A Task Prediction-Augmented Hierarchical Offloading Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260217

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ZHANG Linghao, XU Bo, SUN Jinlong, LAI Haiguang, ZHAO Haitao. A Task Prediction-Augmented Hierarchical Offloading Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260217

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A Task Prediction-Augmented Hierarchical Offloading Method for Space-Air-Ground Integrated Networks

doi: 10.11999/JEIT260217 cstr: 32379.14.JEIT260217

ZHANG Linghao^{1, 2},
XU Bo^{1, 2},
SUN Jinlong^{1, 2},
LAI Haiguang^{1, 2},
ZHAO Haitao^{1, 2
,
,}

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Cowave Communication Technology Co., Ltd., Nanjing 210008, China

Funds: The National Natural Science Foundation of China (No. U2441226), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX24_1197)

Received Date: 2026-03-01
Accepted Date: 2026-06-15
Rev Recd Date: 2026-06-15

Available Online: 2026-06-19

Abstract

Abstract

Objective Space-Air-Ground Integrated Networks (SAGIN) have emerged as a critical infrastructure for future 6G communications, enabling wide-area coverage and flexible deployment through the collaborative operation of Low Earth Orbit (LEO) satellites, Unmanned Aerial Vehicles (UAVs), and ground users (GUs). With the rapid proliferation of Internet of Things (IoT), Internet of Vehicles (IoV), and smart city applications, the volume and diversity of computation-intensive tasks generated by terminal devices have grown substantially, placing stringent demands on real-time computing and resource scheduling. The integration of Mobile Edge Computing (MEC) into SAGIN architectures has enabled near-user computation services by deploying UAVs and satellites as edge computing nodes, effectively reducing task latency. However, achieving efficient task offloading that simultaneously minimizes task completion latency and UAV energy consumption remains a significant challenge. This difficulty arises from the strong coupling among UAV trajectory planning, task offloading decisions, and resource allocation, compounded by the highly dynamic and partially observable nature of SAGIN environments. In particular, existing multi-agent reinforcement learning (MARL) approaches predominantly rely on reactive, instantaneous decision-making without proactive awareness of future task workload variations, leading to decision lag and insufficient adaptability under bursty traffic conditions. To address these challenges, this paper proposes a task prediction-augmented MARL framework that endows agents with forward-looking decision capabilities in dynamic SAGIN environments. Methods The system considers a three-layer SAGIN-MEC architecture comprising one LEO satellite, multiple UAVs, and ground users. Tasks can be processed locally, offloaded to UAVs via Ground-to-Air (G2A) links, or further relayed to the LEO satellite via Air-to-Satellite (A2S) links under a partial offloading mechanism. The joint optimization of UAV trajectory, user association, offloading ratios, and computational resource allocation is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem minimizing the weighted sum of average task latency and UAV flight energy consumption. Given its non-convexity and high dimensionality, the problem is reformulated as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP), upon which a Prediction-Augmented Multi-Agent Proximal Policy Optimization (PA-MAPPO) algorithm is proposed. A lightweight Exponential Smoothing–Autoregressive (ES-AR) prediction module generates multi-step workload forecasts that are incorporated into each agent’s state space. The algorithm adopts a bilevel structure: the outer layer employs Centralized Training and Decentralized Execution (CTDE)-based PA-MAPPO to generate UAV trajectory actions, while the inner layer applies Block Coordinate Descent (BCD) convex optimization to solve resource allocation and offloading subproblems, with closed-form solutions derived via Lagrangian analysis. GAE and PPO-Clip mechanisms ensure training stability and convergence. Results and Discussions Simulations involve 1 LEO satellite, 5 UAVs, and 50 ground users in a 1×1 km² area. PA-MAPPO is compared against MAPPO (without prediction) and PA-MADDPG. Training curves show that PA-MAPPO converges within 500–700 episodes with the highest average reward and smallest variance, demonstrating superior stability (Fig. 3). As the user count increases from 20 to 80, PA-MAPPO consistently maintains the lowest system cost, achieving average reductions of 12.4% and 18.7% relative to MAPPO and PA-MADDPG, respectively (Fig. 4). Experiments varying UAV quantity reveal a U-shaped cost curve for all algorithms, with the optimal configuration at U=5. PA-MAPPO achieves the minimum cost at this point (Fig. 5). Sensitivity analysis over the energy-latency tradeoff weight ω confirms PA-MAPPO’s robustness across different optimization preferences (Fig. 6). The prediction horizon H exhibits a non-monotonic effect on performance, and H=5 yields the optimal result with approximately 14.9% cost reduction over the no-prediction case, while longer horizons degrade performance due to accumulated prediction error (Fig. 7). Conclusions This paper proposes the PA-MAPPO algorithm to address the joint optimization of UAV trajectory planning, user association, task offloading, and computational resource allocation in dynamic SAGIN environments. By introducing a lightweight ES-AR task workload prediction module into the MARL framework, the proposed method equips UAV agents with proactive decision-making capabilities that account for future task dynamics, effectively alleviating the decision lag inherent in purely reactive approaches. The inner BCD-based convex optimization guarantees convergence to a KKT-stationary point, while the outer CTDE-based PPO mechanism ensures training stability and scalability. Simulation results demonstrate that PA-MAPPO achieves significant improvements over baseline methods in terms of average task latency, UAV flight energy consumption, and overall system cost, while exhibiting strong scalability and robustness across varying system configurations. Future work will explore online prediction and decision co-optimization mechanisms in multi-satellite cooperative scenarios, as well as the impact of dynamic network topology changes on algorithm performance.
- Space-Air-Ground Integrated Network,
- task offloading,
- mobile edge computing,
- multi-agent reinforcement learning,
- task prediction

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References(33)

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