A FAST CHANNEL ESTIMATION APPROACH FOR MILLIMETER-WAVE MASSIVE MIMO SYSTEMS

In millimeter-wave massive multiple-input multiple-output systems, to decrease the large training overhead of traditional channel estimation techniques, compressive sensing (CS) is advocated for channel estimation by exploiting the channels' sparse nature. However, existing CS-based channel estimation (CSCE) methods have to deal with a large-size reconstruction problem for sparse channel recovery, which causes high computational cost and long computation time. To overcome these issues, this paper proposes a fast channel estimation (FCE) technique. It utilizes the fact that the sparse structure of the channel matrix can be mapped to reveal useful channel parameters such as the angular and fading information of propagation paths. Based on such mapping, we decouple the channel estimation problem into three sub-problems: angle of arrival detection, angle of departure detection, and path gain estimation. The three sub-problems have reduced size and are solved sequentially, resulting in much lower complexity. Numerical results show that the FCE method runs much faster than the CSCE method to achieve the same accuracy.