中国人民解放军91977部队,北京 100036
收稿:2026-03-06,
修回:2026-04-11,
录用:2026-05-13,
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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/OL]. 电信科学, 2026.
ZHOU Yin. Instant power allocation for terahertz NOMA communication networks based on transformer-double deep Q network[J/OL]. Telecommunications Science, 2026.
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。其核心机制是通过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。为充分释放太赫兹NOMA系统的性能潜力,关键在于实现满足服务质量(quality of service,QoS)约束下的快速功率分配优化。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。经训练后本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。
Terahertz (THz) non-orthogonal multiple access (NOMA) was regarded as a candidate in 6G and beyond systems. By exploring the ultrabroad bandwidth and power domain
THz-NOMA could realize massive connectivity through assigning each sub-band to different users. To unleash the potential of the THz-NOMA system
it was significant to allocate power fast under quality of service (QoS) requirements. Focusing on the instant power allocation
a novel transformer-based double deep Q-network (DQN) solution adaptive for general user distributions was proposed in this paper. Transformer was used to learn the relationships among allocation strategies for different users
and a double DQN was adopted to achieve a more stable decision optimization process. The simulation results validated that the proposed algorithm realized the throughput close to the optimum given by exhaustive search method within millisecond level. The proposed method demonstrates high real-time performance and robustness
which suggests its high practicability.
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