用深度学习联合估计信道与定位，提升稀疏超大规模天线系统性能。
Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems
- 分两阶段：先定位再辅助信道估计，实现协同优化。
- 所提模型在稀疏阵列下信道与定位误差均低于基线方法。
- 适合研究近场大阵列通信系统的工程师与学者。
本文研究近场稀疏超大规模多输入多输出（XL-MIMO）正交频分复用（OFDM）系统中的联合信道估计与定位问题。为实现两者间的协作增益，提出一种基于深度学习的两阶段框架，包含定位与信道估计两个阶段。定位阶段预测用户坐标，并用于辅助后续信道估计，从而提升信道估计精度。在此框架内，提出一种名为CP-Mamba的U型Mamba架构，融合Mamba模型与U型卷积网络结构优势，有效捕捉信道的局部空间特征与长时序依赖关系。数值仿真结果表明，该两阶段方法在使用CP-Mamba架构时显著优于现有基线方法。此外，稀疏阵列（SA）在信道估计与定位精度方面均显著优于传统紧凑阵列。
原文摘要 · Abstract (English)
This paper investigates joint channel estimation and positioning in near-field sparse extra-large multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) systems. To achieve cooperative gains between channel estimation and positioning, we propose a deep learning-based two-stage framework comprising positioning and channel estimation. In the positioning stage, the user's coordinates are predicted and utilized in the channel estimation stage, thereby enhancing the accuracy of channel estimation. Within this framework, we propose a U-shaped Mamba architecture for channel estimation and positioning, termed as CP-Mamba. This network integrates the strengths of the Mamba model with the structural advantages of U-shaped convolutional networks, enabling effective capture of local spatial features and long-range temporal dependencies of the channel. Numerical simulation results demonstrate that the proposed two-stage approach with CP-Mamba architecture outperforms existing baseline methods. Moreover, sparse arrays (SA) exhibit significantly superior performance in both channel estimation and positioning accuracy compared to conventional compact arrays.
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