Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System

Hybrid-field channel estimation for extremely large-scale massive MIMO (XL-MIMO) system is discussed in this letter. By exploiting the structural characteristics of far-field path components in the angle domain and the sparsity of near-field path components in the polar domain, a channel estimation algorithm combining support detection and orthogonal matching pursuit (SD-OMP) is proposed. Specifically, the supports of the far-field path components are firstly detected according to components structure characteristics of the angle domain in the XL-MIMO system and then can be used to obtain the far-field path components. Next, effect on the XL-MIMO system induced by far-field path components can be removed firstly and the OMP algorithm is employed to obtain the near-field path components by exploiting its polar domain sparsity. Finally, the hybrid-field channel is recovered by superposing the given far-field path components and near-field path components. Experiment results show that the proposed algorithm can accurately recover channel with relatively low pilot overhead and computation complexity comparing with some classical channel estimation algorithms.