Decentralized Channel Estimation and User Localization for XL-MIMO Systems

This paper investigates the channel estimation and user localization problem in an extra-large multiple-input multipleoutput (XL-MIMO) system. Unlike existing centralized (highcomplexity) or decentralized all-digital (impractical) approaches, we propose a hybrid analog-digital solution under decentralized baseband processing. Specifically, to reduce the computational complexity, the overall channel estimation problem is decomposed into multiple sub-channel estimation subproblems, each of which is then converted into a low-rank sparse signal recovery problem by exploiting the low-rank and sparse nature of sub-channels. The subproblems are solved via the Riemannian subgradient method working on a fixed-rank manifold. Based on the estimated subchannels, a weighted sub-array joint localization algorithm is designed to localize user position through extracting the angle of arrival of the user's line-of-sight path to each sub-array and combining the spatial position information of the sub-arrays to obtain the user coordinate through cross-location. Simulation results demonstrate superior channel estimation accuracy versus benchmarks, and centimeter-level user positioning ability.

• Publication year

  2025

• Venue

  International Conference on Wireless Communications and Signal Processing

• Publication date

  2025-10-23

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/WCSP68525.2025.1010585
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Channel Estimation for XL-MIMO Systems With Decentralized Baseband Processing: Integrating Local Reconstruction With Global Refinement

  2025cited by this paper
• Convergence Analysis of a Randomized Incremental Subgradient Method on Riemannian Manifolds

  2025cited by this paper
• Unmanned aerial vehicle passive positioning technology based on cross-location and target splitting

  2023cited by this paper
• Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array

  2023cited by this paper
• Sparse Aggregation-Based Channel Estimation For Massive Mimo Systems With Decentralized Baseband Processing

  2023cited by this paper
• Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System

  2023cited by this paper
• An Introduction to Optimization on Smooth Manifolds

  2023cited by this paper
• Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?

  2021cited by this paper
• Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

  2021cited by this paper
• Overview of Match Pursuit Algorithms and Application Comparison in Image Reconstruction

  2021cited by this paper
• Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?

  2021cited by this paper
• Decentralized Equalization With Feedforward Architectures for Massive MU-MIMO

  2018cited by this paper
• Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

  2018cited by this paper
• Sparse channel estimation for MIMO-OFDM systems using compressed sensing

  2016cited by this paper
• Low complexity 2-D DOA estimator for arbitrary arrays: A hybrid MUSIC-based method

  2015cited by this paper
• Spherical Wave Channel and Analysis for Large Linear Array in LoS Conditions

  2015cited by this paper
• Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems

  2015cited by this paper

• No citing papers are available for this paper.