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

arXiv: https://arxiv.org/abs/2507.19936 · HTML: https://mlsanity.com/paper/2507.19936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020)
- [Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2209.12131/) (2022)
- [Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect](https://mlsanity.com/paper/2305.07184/) (2023)
- [Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping](https://mlsanity.com/paper/2308.11289/) (2023)
- [Can Sparse Arrays Outperform Collocated Arrays for Future Wireless Communications?](https://mlsanity.com/paper/2307.07925/) (2023)
- [Exploiting Active RIS in NOMA Networks with Hardware Impairments](https://mlsanity.com/paper/2311.14295/) (2023)
- [Wideband Beamforming for RIS Assisted Near-Field Communications](https://mlsanity.com/paper/2401.11141/) (2024)
- [Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks](https://mlsanity.com/paper/2401.14219/) (2024)
- [Sparse Estimation for XL-MIMO with Unified LoS/NLoS Representation](https://mlsanity.com/paper/2403.12506/) (2024)
- [Channel Estimation for Pinching-Antenna Systems (PASS)](https://mlsanity.com/paper/2503.13268/) (2025)
- [Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm](https://mlsanity.com/paper/2504.13570/) (2025)

## Abstract (arXiv)

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.