基于行为克隆的高通量卫星通信频谱资源分配

秦浩; 李双益; 赵迪; 孟昊炜; 宋彬

doi:10.11959/j.issn.1000-436x.2024100

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基于行为克隆的高通量卫星通信频谱资源分配

学术论文 | 更新时间：2024-06-24

- 基于行为克隆的高通量卫星通信频谱资源分配
- Spectrum resource allocation for high-throughput satellite communications based on behavior cloning
- 通信学报 2024年45卷第5期页码：101-114
- 作者机构：
  
  1.西安电子科技大学空天地一体化综合业务网全国重点实验室,陕西西安 710071
  2.西安电子科技大学杭州研究院,浙江杭州 311200
- 作者简介：
  
  [ "秦浩（1976- ），男，陕西绥德人，博士，西安电子科技大学副教授、硕士生导师，主要研究方向为卫星通信星地通信体制、无线通信和卫星通信智能资源管控。" ]
  [ "李双益（2001- ），男，河南许昌人，西安电子科技大学硕士生，主要研究方向为卫星通信和无线通信。" ]
  [ "赵迪（1995- ），女，山东淄博人，西安电子科技大学博士生，主要研究方向为卫星通信、无线资源管理和机器学习。" ]
  [ "孟昊炜（1998- ），男，河南周口人，西安电子科技大学硕士生，主要研究方向为卫星通信和无线通信。" ]
  宋彬（1973- ），男，河南郑州人，博士，西安电子科技大学教授、博士生导师，主要研究方向为多媒体通信、多模态数据融合与检索、基于图像内容的识别与机器学习、多模态知识图谱、强化学习、物联网、大数据和推荐系统。bsong@mail.xidian.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62071354;62201419);陕西省重点研发计划基金资助项目(2022ZDLGY 05-08)
- DOI：10.11959/j.issn.1000-436x.2024100
  中图分类号： TN92
- 收稿日期：2024-02-05，
  
  修回日期：2024-05-07，
  
  纸质出版日期：2024-05-30
- 稿件说明：
移动端阅览
秦浩,李双益,赵迪等.基于行为克隆的高通量卫星通信频谱资源分配[J].通信学报,2024,45(05):101-114.

QIN Hao,LI Shuangyi,ZHAO Di,et al.Spectrum resource allocation for high-throughput satellite communications based on behavior cloning[J].Journal on Communications,2024,45(05):101-114.
秦浩,李双益,赵迪等.基于行为克隆的高通量卫星通信频谱资源分配[J].通信学报,2024,45(05):101-114. DOI： 10.11959/j.issn.1000-436x.2024100.

QIN Hao,LI Shuangyi,ZHAO Di,et al.Spectrum resource allocation for high-throughput satellite communications based on behavior cloning[J].Journal on Communications,2024,45(05):101-114. DOI： 10.11959/j.issn.1000-436x.2024100.

摘要

为应对在高通量多波束卫星系统中，随着波束数量和用户规模的扩大，频谱资源分配问题的维度急剧增加和求解复杂度呈指数级上升这一挑战，提出了一种结合行为克隆与深度强化学习的两阶段算法。第一阶段基于行为克隆，利用已有卫星资源分配决策数据对策略网络进行预训练，通过模仿专家行为减少盲目探索，加快算法收敛。第二阶段基于近端策略优化，进一步优化策略网络，并通过引入卷积注意力模块有效地提取用户业务状态特征，以提升算法整体性能。仿真结果表明，所提算法在收敛速度和算法稳定性方面均优于其他基准算法，并在系统时延、系统平均满意度和频谱效率等性能指标上表现更佳。

Abstract

In high-throughput multi-beam satellite systems

the dimensionality of the spectrum resource allocation problem increased drastically with the number of satellite beams and service users

which caused an exponential rise in the complexity of the solution. To address the challenge

a two-stage algorithm that combined behavior cloning (BC) with deep reinforcement learning (DRL) was proposed. In the first stage

the strategy network was pretrained using existing decision data from satellite operation through behavior cloning

which mimicked expert behavior to reduce blind exploration and accelerate algorithm convergence. In the second stage

the strategy network was further optimized using the proximal policy optimization (PPO)

and a convolutional block attention module (CBAM) was employed to better extract the user traffic features

thereby enhancing overall algorithm performance. Simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of convergence speed and algorithm stability

and also delivers superior performance in system delay

average system satisfaction

and spectrum efficiency.

关键词

Keywords

references

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