arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.20178eess.SPcs.ITcs.LGmath.IT
收藏

数据驱动的深度多用户多输入多输出检测：网络架构与泛化分析

Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis

School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）

Yongwei Yi, Xinping Yi, Wenjin Wang, Xiao Li, Shi Jin

备注：
17 pages, 7 figures. Full version of a work prepared for submission to IEEE
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出了一种基于图神经网络的深度多用户多输入多输出检测方法GNNSIC，通过参数共享提升泛化能力和样本效率。

摘要：

在实际的多用户多输入多输出（MU-MIMO）系统中，符号检测仍然具有挑战性，因为存在严重的用户间干扰和对信道状态信息（CSI）不确定性的敏感性。与大多数研究的信念传播型模型驱动方法相比，后者计算复杂度高，而软干扰抵消（SIC）在性能和复杂性之间取得了良好的平衡。为了进一步解决CSI不匹配和非线性效应，最近提出的数据驱动深度神经接收机，如DeepSIC，利用深度神经网络的优势进行干扰抵消和符号检测，表现出强大的经验性能。然而，仍然缺乏理论依据来解释为什么以及到何种程度DeepSIC能够随着训练样本数量的增加而泛化。本文提出在网络-MLP架构中检查完全数据驱动的DeepSIC检测，该架构由多个通过外层和内层有向无环图（DAG）连接的MLP组成。在这样的架构中，DeepSIC可以升级为基于图的消息传递过程，使用图神经网络（GNNs），称为GNNSIC，具有跨用户和迭代共享的模型参数。值得注意的是，GNNSIC在表达能力上与DeepSIC相当，但可训练参数显著更少，从而提高了样本效率和用户泛化能力。通过使用Rademacher复杂性进行基于范数的泛化分析，我们揭示了由于参数共享，DeepSIC在迭代次数上的指数依赖关系可以被消除。仿真结果表明，GNNSIC在显著更少的参数和训练样本下，实现了与DeepSIC相当或改进的符号错误率（SER）性能。

英文摘要：

In practical Multiuser Multiple-Input Multiple-Output (MU-MIMO) systems, symbol detection remains challenging due to severe inter-user interference and sensitivity to Channel State Information (CSI) uncertainty. In contrast to the mostly studied belief propagation-type model-driven methods, which incur high computational complexity, Soft Interference Cancellation (SIC) strikes a good balance between performance and complexity. To further address CSI mismatch and nonlinear effects, the recently proposed data-driven deep neural receivers, such as DeepSIC, leverage the advantages of deep neural networks for interference cancellation and symbol detection, demonstrating strong empirical performance. However, there is still a lack of theoretical underpinning for why and to what extent DeepSIC could generalize with the number of training samples. This paper proposes inspecting the fully data-driven DeepSIC detection within a Network-of-MLPs architecture, which is composed of multiple interconnected MLPs via outer and inner Directed Acyclic Graphs (DAGs). Within such an architecture, DeepSIC can be upgraded as a graph-based message-passing process using Graph Neural Networks (GNNs), termed GNNSIC, with shared model parameters across users and iterations. Notably, GNNSIC achieves excellent expressivity comparable to DeepSIC with substantially fewer trainable parameters, resulting in improved sample efficiency and enhanced user generalization. By conducting a norm-based generalization analysis using Rademacher complexity, we reveal that an exponential dependence on the number of iterations for DeepSIC can be eliminated in GNNSIC due to parameter sharing. Simulation results demonstrate that GNNSIC attains comparable or improved Symbol Error Rate (SER) performance to DeepSIC with significantly fewer parameters and training samples.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑