用图神经网络与强化学习优化太赫兹卫星网络的资源分配与任务卸载。
Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks
- 基于图神经网络捕捉卫星间连接关系，实现分布式智能决策。
- 相比基准方案，资源效率更高且延迟更低，训练参数更少、运行更快。
- 适合研究太赫兹卫星通信与边缘计算的科研人员和工程师。
太赫兹空间通信（Tera-SpaceCom）被视为支持太空科学与通信应用的前沿技术，涵盖太赫兹传感、空间数据中心提供云服务，以及低轨巨型星座通过太赫兹链路将任务转发至地面站或数据中心。为减轻数据中心计算负担及中继过程中的资源消耗与延迟，低轨星座提供卫星边缘计算（SEC）服务，使卫星可直接处理探索任务。接收任务的卫星将部分任务卸载给邻近卫星以分担计算压力。然而，由于任务与子阵列的离散性及发射功率的连续性，该场景下的联合资源分配与任务卸载属于NP难混合整数非线性规划问题（MINLP）。为此，提出一种基于图神经网络（GNN）与深度强化学习（DRL）的联合资源分配与任务卸载算法（GRANT），旨在提升长期资源效率（RE）。GNN从卫星连通性中学习其相互关系，多智能体多任务机制协同训练卸载与资源分配策略。实验表明，相较于基准方案，GRANT在保持较低延迟的同时实现了最高资源效率，且模型参数最少、运行时间最短。
原文摘要 · Abstract (English)
Terahertz (THz) space communications (Tera-SpaceCom) is envisioned as a promising technology to enable various space science and communication applications. Mainly, the realm of Tera-SpaceCom consists of THz sensing for space exploration, data centers in space providing cloud services for space exploration tasks, and a low earth orbit (LEO) mega-constellation relaying these tasks to ground stations (GSs) or data centers via THz links. Moreover, to reduce the computational burden on data centers as well as resource consumption and latency in the relaying process, the LEO mega-constellation provides satellite edge computing (SEC) services to directly compute space exploration tasks without relaying these tasks to data centers. The LEO satellites that receive space exploration tasks offload (i.e., distribute) partial tasks to their neighboring LEO satellites, to further reduce their computational burden. However, efficient joint communication resource allocation and computing task offloading for the Tera-SpaceCom SEC network is an NP-hard mixed-integer nonlinear programming problem (MINLP), due to the discrete nature of space exploration tasks and sub-arrays as well as the continuous nature of transmit power. To tackle this challenge, a graph neural network (GNN)-deep reinforcement learning (DRL)-based joint resource allocation and task offloading (GRANT) algorithm is proposed with the target of long-term resource efficiency (RE). Particularly, GNNs learn relationships among different satellites from their connectivity information. Furthermore, multi-agent and multi-task mechanisms cooperatively train task offloading and resource allocation. Compared with benchmark solutions, GRANT not only achieves the highest RE with relatively low latency, but realizes the fewest trainable parameters and the shortest running time.
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