Gridless Hybrid-Field Channel Estimation for Extra-Large Aperture Array Massive MIMO Systems

Xi Yang, Fuqiang Zhu, Binggui Zhou, Ting Liu, Shaodan Ma

Abstract: Channel estimation is significant for extra-large aperture array (ELAA) massive multiple-input multiple-output (MIMO) systems to fully fulfill their potential. However, hybrid-field propagation environment appears due to adopting ELAA, which thereby severely degrades the performance of existing channel estimation algorithms designed based on the channel sparsity property in traditional beam/angle domains. To tackle this problem, we propose a gridless hybrid-field channel estimation algorithm in this letter by excavating the hybrid-field channel sparsity in the fractional Fourier domain. The hybrid-field channel estimation problem is first formulated, and then the discrete fractional Fourier transform (DFrFT) is introduced to reveal the channel sparsity in the fractional Fourier domain. After that, the DFrFT-based Newtonized orthogonal matching pursuit algorithm is proposed without prior knowledge of the number of propagation paths. Numerical results show that the proposed algorithm greatly outperforms the existing algorithms.