Channel Estimation for Extremely Large-Scale Massive MIMO Systems in Hybrid-Field Channel

Abstract

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is the key technique for future 6G communications. However, the extremely large aperture arrays make the accurate channel state information (CSI) acquisition more difficult. To solve this problem, we focus on the location information (i.e., the angle and range) of the scatters in the far-field and near-field areas, and propose a hybrid-field channel estimation algorithm for the XL-MIMO system. Specifically, firstly by simplifying the hybrid-field channel model, we transform the channel estimation problem into the position parameters estimation problem for the far-field and near-field scatters. Then, we propose a hybrid-field channel estimation algorithm based on the estimation of the angle and range of the different scatters. Finally, numerical results show the superior performances of the proposed algorithm in comparison to the existing methods.

Jingjing Liu

Associate Professor

My research interests include low-power smart micro-sensor integrated circuit design, image sensors, biomedical sensors, and energy harvesting circuits.