标题： 基于深度学习的稀疏XL-MIMO OFDM系统的联合信道估计与定位 显示英文标题

标题： Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems

摘要： 本文研究了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和定位问题。 为了实现信道估计和定位之间的协作增益，我们提出了一种基于深度学习的两阶段框架，包括定位和信道估计。 在定位阶段，预测用户的坐标并在信道估计阶段加以利用，从而提高信道估计的准确性。 在此框架中，我们提出了一种用于信道估计和定位的U型Mamba架构，称为CP-Mamba。 该网络结合了Mamba模型的优势与U型卷积网络的结构优势，能够有效捕捉信道的局部空间特征和远距离时间依赖性。 数值仿真结果表明，所提出的具有CP-Mamba架构的两阶段方法优于现有的基线方法。 此外，稀疏阵列（SA）在信道估计和定位精度方面相比传统紧凑阵列表现出显著优越的性能。 显示英文摘要

摘要： 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.