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

TL;DR

This paper introduces a deep learning framework for joint channel estimation and positioning in XL-MIMO OFDM systems, leveraging a novel U-shaped Mamba architecture to improve accuracy and outperform existing methods.

Contribution

The paper proposes a two-stage deep learning framework with a new CP-Mamba architecture for enhanced joint channel estimation and positioning in near-field XL-MIMO OFDM systems.

Findings

The proposed approach outperforms baseline methods in simulations.

Sparse arrays significantly improve estimation and positioning accuracy.

The CP-Mamba architecture effectively captures spatial and temporal features.

Abstract

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.