Sparse hybrid-field channel estimation in massive MIMO systems

Abstract Massive multiple-input multiple-output (MIMO) technology is a key enabler for 5G and beyond networks, particularly in the evolution toward 6G. The integration of millimeter-wave (mmWave) and terahertz (THz) frequencies introduces a hybrid communication paradigm that encompasses both near-field and far-field propagation, posing significant challenges for accurate channel estimation. Traditional hybrid-field channel estimation methods rely on idealized assumptions of uniform scatterer distributions and stationary conditions. However, real-world environments feature irregular scatterer distributions and mixed mobility scenarios, which degrade the performance of existing techniques. This paper proposes the weighted dynamic hybrid-field simultaneous orthogonal matching pursuit (WDHF-SOMP) algorithm, an extension of AHF-SOMP, to address these limitations. The proposed algorithm introduces a weighted support selection strategy to model irregular scatterer distributions and dynamic mobility, enabling more accurate hybrid-field channel estimation. The WDHF-SOMP algorithm is evaluated under diverse conditions, including high path loss environments, multipath-rich channels, and interference scenarios. Simulation results demonstrate that WDHF-SOMP achieves normalized mean square error (NMSE) gains ranging from 0.3 dB in antenna scaling scenarios to 2 dB in irregular scatterer environments at 10 dB SNR. The gain holds across diverse scenarios with severe fading and pilot contamination. The findings demonstrate the effectiveness of WDHF-SOMP in enhancing spectral efficiency and robustness in massive MIMO systems, making it a promising solution for next-generation wireless networks.