发表机构
School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种星地协同推理系统，通过联合优化DNN分区与资源分配，并采用两层优化算法，在能量约束下最小化平均任务完成时延，仿真验证其优于传统二进制卸载。
AI 中文摘要
本文研究了一种用于遥感信息处理的星地协同推理系统，其中地面站（GS）为运行异构深度神经网络（DNN）推理任务的多颗低地球轨道（LEO）卫星提供服务。通过在卫星与地面站之间对DNN进行分区，每颗卫星在本地执行前端层，将生成的中间特征传输至地面站，从而将剩余层卸载至地面站以完成推理。我们联合优化DNN分区、卫星与地面站计算资源、星地带宽分配以及卫星发射功率，以在每颗卫星能量约束下最小化平均任务完成时延。为解决离散分区决策与连续通信计算变量之间的耦合问题，我们提出了一种两层优化算法。内层采用闭式更新和高效的嵌套二分搜索来更新连续资源变量，外层采用随机重启坐标下降法来优化分区决策。仿真结果表明，与传统二进制卸载相比，所提算法有效降低了任务完成时延。
英文摘要
This letter investigates a satellite-terrestrial collaborative inference system for remote sensing information processing, where a ground station (GS) serves multiple low Earth orbit (LEO) satellites running heterogeneous deep neural network (DNN) inference tasks. By partitioning the DNN between the satellite and the GS, each satellite executes the front-end layers locally, transmits the resulting intermediate features to the GS, thereby offloading the remaining layers to the GS for inference completion. We jointly optimize DNN partitioning, satellite and GS computing resources, satellite-ground bandwidth allocation, and satellite transmit power to minimize the average task completion latency under per-satellite energy constraints. To address the coupling between discrete partitioning decisions and continuous communication-computing variables, we propose a two-layer optimization algorithm. The inner layer performs closed-form updates and efficient nested bisection searching to update the continuous resource variables, while the outer layer employs random-restart coordinate descent to refine the partitioning decisions. Simulation results show that the proposed algorithm effectively reduces task completion latency compared with conventional binary offloading.
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