NOMA-Assisted Pipeline Parallel Inference for Satellite-UAV Collaborative Networks

YANG Mengqi; WU Yiman; ZHOU Renfei; WU Peng; PAN Peng; ZHENG Changliang

doi:10.11959/j.issn.1000-0801.DXKX260294

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NOMA-Assisted Pipeline Parallel Inference for Satellite-UAV Collaborative Networks

更新时间：2026-06-23

- NOMA-Assisted Pipeline Parallel Inference for Satellite-UAV Collaborative Networks
- Telecommunications Science (2026)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.中国人民解放军31007部队，北京 100079
  3.中国人民解放军军事科学院国防科技创新研究院，北京 100071
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62401612)
- DOI：10.11959/j.issn.1000-0801.DXKX260294
  CLC： TN927
- Received：09 May 2026，
  
  Revised：2026-05-31，
  
  Accepted：23 June 2026，
- 稿件说明：
移动端阅览
YANG Mengqi, WU Yiman, ZHOU Renfei, et al. NOMA-Assisted Pipeline Parallel Inference for Satellite-UAV Collaborative Networks[J/OL]. Telecommunications Science, 2026.
DOI：

YANG Mengqi, WU Yiman, ZHOU Renfei, et al. NOMA-Assisted Pipeline Parallel Inference for Satellite-UAV Collaborative Networks[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260294.

摘要

低轨卫星与无人机协同推理为实时智能感知任务提供了新的解决途径。针对现有协同计算方法中任务卸载或非流水线模型切分易造成通信与计算相互等待、资源利用效率较低的问题，本文提出一种结合模型切分与流水线并行的卫星无人机协同推理方案。该方案对深度神经网络进行切分，其中头部和尾部部署于无人机侧，计算密集的中间子网络部署于卫星侧，并通过中间特征传递实现完整推理功能。在此基础上，所提方案引入微批次流水线并行机制与非正交多址接入（non-orthogonal multiple access，NOMA）技术，使不同微批次的计算和传输阶段并行执行。进一步，以端到端协同推理时延最小化为目标，联合优化了神经网络部署结构、微批次划分粒度及上下行传输功率。仿真结果表明，所提方案能够有效降低协同推理时延，提升系统资源利用率。

Abstract

Collaborative inference between low-Earth-orbit (LEO) satellites and unmanned aerial vehicles (UAVs) provides a new solution for real-time intelligent sensing tasks. Existing collaborative computing methods based on task offloading or non-pipelined model partitioning tend to cause mutual waiting between communication and computation

resulting in low resource efficiency. To address this problem

a satellite-UAV collaborative inference scheme combining model splitting and pipeline parallelism was proposed. In this scheme

the deep neural network was partitioned

with the head and tail subnetworks deployed on the UAVs and the computation-intensive intermediate subnetwork deployed on the satellite. Complete inference was achieved through intermediate feature transmission. On this basis

the micro-batch pipeline parallel mechanism and non-orthogonal transmission were employed

allowing the computation and transmission stages of different micro-batches to be executed in parallel. Furthermore

with the objective of minimizing the end-to-end collaborative inference delay

the neural network deployment structure

micro-batch partitioning granularity

and uplink and downlink transmission power were jointly optimized. Simulation results showed that the proposed scheme could effectively reduce the collaborative inference delay and improve system resource utilization.

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references

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