arXiv：cs.LG· Kaijun Feng, Jiaxin He, Hongrui Yu, Zhen Gao, Anwen Liao, Ziwei Wan, Zhaocheng Wang·· 4 小时前AI 评分24
基于深度学习的 Tri-Hybrid 多用户 MIMO 预编码：EM 可重构天线带来的增益
Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas
AI 导读
研究者提出基于 Conformer 的三混合预编码网络 Tri-PNet，将 EM 域可重构天线预编码与常规混合模拟-数字预编码联合设计，以最大化平均和频谱效率。
正文
Abstract:Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, with conventional hybrid analog-digital precoding yields tri-hybrid multiple-input multiple-output (MIMO) precoding, which can substantially improve the spectral efficiency of wideband multi-user MIMO orthogonal frequency-division multiplexing (OFDM) systems. However, the joint design of EM, analog, and digital precoding remains challenging. To address this challenge, we propose a tri-hybrid precoding network (Tri-PNet) based on Conformer, an emerging neural architecture that combines the local modeling strength of convolutional neural networks with the global dependency modeling of Transformers. Furthermore, two representative radiation-pattern modes, i.e., the non-regular mode and the 3rd Generation Partnership Project (3GPP) Technical Report (TR) 38.901 mode, are investigated. Tri-PNet is trained in an unsupervised manner to jointly learn EM, analog, and digital precoding by maximizing the average sum spectral efficiency. Its radiation-pattern selection network (RPSNet) employs a Conformer encoder to capture both local and global frequency-domain correlations, whereas its hybrid analog-digital precoding network (HPNet) combines cross-attention and dual-path processing with singular-value-decomposition (SVD) and zero-forcing (ZF) priors. Simulation results under both radiation-pattern modes demonstrate that Tri-PNet outperforms random EM precoding and conventional hybrid MIMO without EM precoding, approaches the greedy EM precoding search scheme with substantially lower online complexity, and remains robust to imperfect channel state information (CSI).
| Comments: | 14 pages, 13 figures, 4 tables |
| Subjects: | Signal Processing (eess.SP); Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.39167 [eess.SP] |
| (or arXiv:2609.39167v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39167 arXiv-issued DOI via DataCite |
Submission history
From: Zhen Gao [view email]
[v1]
Wed, 30 Sep 2026 07:28:04 UTC (2,412 KB)
来源：arXiv：cs.LG · arxiv.org