面向大规模MIMO检测的图基础模型
A Graph Foundation Model for Large-Scale MIMO Detection
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中文总结 AI 辅助
提出一种无线原生图基础模型（GFM），结合消息传递与图Transformer及期望传播物理信息，通过预训练和参数高效微调，实现大规模MIMO检测的准确、泛化与跨场景迁移。
中文摘要 AI 辅助
大规模多输入多输出（MIMO）检测是现代无线网络的基础，但受限于性能与复杂度之间的权衡。现有检测器，无论是经典的还是基于学习的，在异构场景中往往在可扩展性或泛化能力方面存在不足。为克服这些局限，我们提出了一种面向大规模MIMO检测的无线原生图基础模型（GFM）。该GFM采用物理信息混合架构，将消息传递神经网络的局部相关性提取与图Transformer的全局注意力相结合，编码来自期望传播算法的物理干扰模式。通过大规模预训练，这种协同作用使得学习一种通用的检测映射成为可能，该映射可跨天线维度和信道条件进行扩展。为快速下游部署，利用参数高效微调，以最小开销将GFM适配到特定的非理想系统场景。为提升推理效率，在下游部署中嵌入混合专家机制，仅动态激活必要的子模块。评估表明，所提出的GFM在准确性、配置泛化性以及跨各种具有挑战性的零样本和少样本条件下的跨场景迁移性方面，始终优于经典检测器和先进的数据驱动基线。
英文摘要
Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whether classical or learning-based, often fall short in either scalability or generalizability across heterogeneous scenarios. To overcome these limitations, we introduce a wireless-native graph foundation model (GFM) tailored for large-scale MIMO detection. The proposed GFM employs a physics-informed hybrid architecture, integrating the local correlation extraction of message passing neural networks with the global attention of graph Transformers, encoding the physical interference patterns from the expectation propagation algorithm. Via extensive pre-training, this synergy enables the learning of a general-purpose detection mapping scalable across antenna dimensions and channel conditions. For rapid downstream deployment, parameter-efficient fine-tuning is leveraged to adapt the GFM to specific non-ideal system regimes with minimal overhead. To enhance inference efficiency, a mixture-of-experts mechanism is embedded at downstream deployment to dynamically activate only the necessary sub-modules. Evaluations show that the proposed GFM consistently outperforms classical detectors and advanced data-driven baselines in accuracy, configuration generality, and cross-scenario transferability across various challenging zero-shot and few-shot conditions.
发表机构
- National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）
- Department of Electrical and Computer Engineering, University of California, Santa Cruz（加州大学圣克鲁兹分校电气与计算机工程系）
- Institute of Communications Engineering, National Sun Yat-sen University（国立中山大学通信工程研究所）
- School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）
机构由 AI 辅助整理，请以论文原文为准。
