Fast and Robust Channel Estimation for HMIMO: A Graph-Based Wavenumber-Domain Approach

This paper proposes a fast and robust graph-based wavenumber-domain approach for channel estimation in holo-graphic MIMO (HMIMO) systems. Unlike conventional angulardomain methods—prone to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mutual coupling, power leakage</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sampling redundancy</i>—our framework resolves HMIMO’s high-dimensional challenges by introducing a wavenumber-domain basis via orthogonal Fourier harmonics (FHs), eliminating dependencies on antenna density. By reformulating channel estimation as its sparse recovery counterpart, we model clustered sparsity using an elliptic Markov random field (EMRF), upon which a graph-cut swap expansion (GCSE) algorithm is developed, leveraging graph-theoretic optimizations for fast convergence and low complexity. Simulations demonstrate that our method achieves robust performance against mutual coupling, varying SNRs, and antenna density with drastically less computing time.