A Convex Demixing Approach for Hybrid-Field Channel Estimation of XL-MIMO Systems via Atomic Norm Minimization
2025/09/23 by Yang, Dehui, Xi, Feng, Zhu, Yanxian
#FOS: Computer and information sciences #Information Theory (cs.IT)
paper · doi:10.48550/arxiv.2509.18752
Abstract
Channel estimation is a critical task in extremely large-scale multiple-input multiple-output (XL-MIMO) systems for 6G wireless communications. A hybrid-field channel model effectively characterizes the mixed far-field and near-field scattering components in practical XL-MIMO systems. In this paper, we propose a convex demixing approach for hybrid-field channel estimation within the atomic norm minimization (ANM) framework. By promoting sparsity of the far-field and near-field components directly in the continuous parameter domain, a demixing scheme that minimizes a weighted sum of two atomic norms is proposed. We show that the resulting ANM is equivalent to a computationally feasible semi-definite programming (SDP). Numerical experiments on simulated data demonstrate that our method outperforms existing approaches for hybrid-field channel estimation.
Citations
- Hybrid-Field Channel Estimation for XL-MIMO Systems with Stochastic Gradient Pursuit Algorithm
- A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications
- Near-Field MIMO Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions
- Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
- Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?
- Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?
- A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
- Super-Resolution of Complex Exponentials from Modulations with Unknown Waveforms
- Vandermonde Decomposition of Multilevel Toeplitz Matrices with Application to Multidimensional Super-Resolution
- Off-the-Grid Line Spectrum Denoising and Estimation with Multiple Measurement Vectors
- Exact Joint Sparse Frequency Recovery via Optimization Methods
- Compressed Sensing off the Grid
- Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences
Cited by
Related