基于Transformer-双Q网络的太赫兹NOMA通信网络即时功率分配

周音

doi:10.11959/j.issn.1000-0801.DXKX260150

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基于Transformer-双Q网络的太赫兹NOMA通信网络即时功率分配

研究与开发 | 更新时间：2026-07-21

- 基于Transformer-双Q网络的太赫兹NOMA通信网络即时功率分配
- Instant power allocation for terahertz NOMA communication networks based on Transformer-double deep Q-network
- 电信科学 2026年42卷第6期页码：119-130
- 作者机构：
  
  中国人民解放军91977部队，北京 100036
- 作者简介：
  
  周音（1980- ），女，中国人民解放军91977部队高级工程师，主要研究方向为无线通信、军事通信、数据链技术等。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260150
  中图分类号： TN92
- 收稿：2026-03-06，
  
  修回：2026-04-11，
  
  录用：2026-05-18，
  
  网络首发：2026-07-21，
  
  纸质出版：2026-06-20
- 稿件说明：
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周音.基于Transformer-双Q网络的太赫兹NOMA通信网络即时功率分配[J].电信科学,2026,42(06):119-130.

Zhou Yin.Instant power allocation for terahertz NOMA communication networks based on Transformer-double deep Q-network[J].Telecommunications Science,2026,42(06):119-130.
周音.基于Transformer-双Q网络的太赫兹NOMA通信网络即时功率分配[J].电信科学,2026,42(06):119-130. DOI： 10.11959/j.issn.1000-0801.DXKX260150.

Zhou Yin.Instant power allocation for terahertz NOMA communication networks based on Transformer-double deep Q-network[J].Telecommunications Science,2026,42(06):119-130. DOI： 10.11959/j.issn.1000-0801.DXKX260150.

摘要

太赫兹非正交多址接入（non-orthogonal multiple access，NOMA）技术有望成为6G通信系统的关键突破性方案。其核心机制是利用超宽带资源与功率域复用，实现海量用户共享多个子频段，显著提升系统连接容量。要充分释放太赫兹NOMA系统的性能潜力，关键在于要满足服务质量（quality of service，QoS）约束下的快速功率分配优化要求。为此，提出了一种基于Transformer架构的双Q网络模型，通过Transformer学习不同用户分配策略的关联性，并采用双Q网络实现更稳定的决策优化过程。经训练后，该算法可生成适应多种用户分布的即时功率分配策略。实验结果表明，训练完成的模型仅需要毫秒级计算时间，即可实现接近穷举法的高吞吐量性能。该算法展现出较强的实时性与鲁棒性，具有较大工程应用潜力。

Abstract

Terahertz (THz) non-orthogonal multiple access (NOMA) is regarded as a candidate in 6G and beyond systems. By exploiting the ultrabroad bandwidth and power domain

THz-NOMA can realize massive connectivity by assigning each sub-band to different users. To unleash the potential of the THz-NOMA system

it is significant to allocate power quickly under quality of service (QoS) requirements. Focusing on the instant power allocation

a novel Transformer-based double deep Q-network solution adaptive to general user distributions was proposed. Transformer was used to learn the relationships among allocation strategies for different users

and a double deep

Q-network was adopted to achieve a more stable decision optimization process. The simulation results validate that the proposed algorithm realized the throughput close to the optimum given by exhaustive search method within the millisecond level. The proposed method demonstrates high real-time performance and robustness

which suggests its high practicability.

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Keywords

references

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