用轻量深度学习模型解决多基站大规模MIMO预编码复杂度问题
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites
- 采用师生架构与元学习提升跨场景泛化能力
- 无需矩阵求逆，计算复杂度降低73倍以上
- 在未见站点上仍保持高吞吐量，适合实际部署
大规模多输入多输出（mMIMO）技术通过提升频谱效率和网络容量重塑了无线通信。本文提出一种基于深度学习的mMIMO预编码器，旨在解决现有方法（如加权最小均方误差，WMMSE）的计算复杂度问题。通过引入元学习域泛化与师生架构，模型在未见过的基站站点上仍能实现优异的总吞吐率性能，且避免矩阵求逆，采用更简洁的神经网络结构。模型在自建的射线追踪数据集（包含多个基站位置）上训练与测试。实验表明，该方法在计算效率与高吞吐量之间取得良好平衡，并在未见环境中表现出强泛化能力。进一步微调后，所提模型在所有测试站点及信噪比条件下均超越WMMSE，同时计算复杂度降低至少73倍。
原文摘要 · Abstract (English)
Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches, such as weighted minimum mean square error (WMMSE), while leveraging meta-learning domain generalization and a teacher-student architecture to improve generalization across diverse communication environments. When deployed to a previously unseen site, the proposed model achieves excellent sum-rate performance while maintaining low computational complexity by avoiding matrix inversions and by using a simpler neural network structure. The model is trained and tested on a custom ray-tracing dataset composed of several base station locations. The experimental results indicate that our method effectively balances computational efficiency with high sum-rate performance while showcasing strong generalization performance in unseen environments. Furthermore, with fine-tuning, the proposed model outperforms WMMSE across all tested sites and SNR conditions while reducing complexity by at least 73$\times$.
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