Channel Estimation for Large Intelligent Surface-Based Transceiver Using
a Parametric Channel Model
Abstract
Large intelligent surface-based transceivers (LISBTs), in which a
spatially continuous surface is being used for signal transmission and
reception, have emerged as a promising solution for improving the
coverage and data rate of wireless communication systems. To realize
these objectives, the acquisition of accurate channel state information
(CSI) in LISBT-assisted wireless communication systems is crucial. In
this paper, we propose a channel estimation scheme based on a parametric
physical channel model for line-of-sight dominated communication in
millimeter and terahertz wave bands. The proposed estimation scheme
requires only five pilot signals to perfectly estimate the channel
parameters assuming there is no noise at the receiver. In the presence
of noise, we propose an iterative estimation algorithm that decreases
the channel estimation error due to noise. The training overhead and
computational cost of the proposed scheme do not scale with the number
of antennas. The simulation results demonstrate that the proposed
estimation scheme significantly outperforms other benchmark schemes.