Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems
This work addresses channel estimation and positioning for XL-MIMO systems, which is incremental as it builds on existing deep learning and Mamba models for a specific wireless communication domain.
The paper tackles joint channel estimation and positioning in near-field sparse XL-MIMO OFDM systems by proposing a deep learning-based two-stage framework with a CP-Mamba architecture, which outperforms baseline methods and shows sparse arrays significantly improve accuracy over compact arrays.
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.