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

• URL: http://arxiv.org/abs/2507.19936v1
• Date: Sat, 26 Jul 2025 12:47:39 GMT
• Title: Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems
• Authors: Zhongnian Li, Chao Zheng, Jian Xiao, Ji Wang, Gongpu Wang, Ming Zeng, Octavia A. Dobre,
• Abstract summary: We propose a deep learning-based two-stage framework comprising positioning and channel estimation.<n>In the positioning stage, the user's coordinates are predicted and utilized in the channel estimation stage.<n>Within this framework, we propose a U-shaped Mamba architecture for channel estimation and positioning.
• Score: 25.910259131859185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information [70.25632840894272]
  This paper investigates the near-field (NF) channel estimation for large-scale multiple-input multiple-output (XL-MIMO) systems.<n>We propose a GenAI-based approach to refine the estimated channel.<n> Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE.
  arXiv  Detail & Related papers  (2025-05-11T08:35:36Z)
• Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems [46.42375183269616]
  We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) modulation aided systems. Both our simulation results and analysis demonstrate that the proposed channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes.
  arXiv  Detail & Related papers  (2024-03-06T15:05:39Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• CNN based Channel Estimation using NOMA for mmWave Massive MIMO System [0.0]
  This paper proposes a convolutional neural network based approach to estimate the channel for millimeter wave (mmWave) systems built on a hybrid architecture. A coarse estimation of the channel is first made from the received signal. Numerical illustrations show that the proposed method outperforms least square (LS) estimate, minimum mean square error (MMSE) estimate and are close to the Cramer-Rao Bound (CRB)
  arXiv  Detail & Related papers  (2021-08-01T05:33:55Z)
• Learning to Estimate RIS-Aided mmWave Channels [50.15279409856091]
  We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.
  arXiv  Detail & Related papers  (2021-07-27T06:57:56Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.