Compressive Sensing for Indoor THz Channel Estimation

Terahertz (THz) communication is a new emerging technology which has the potential to satisfy the steadily growing demands for high data rates. However, transmission over the THz channel is a difficult task since perfect channel state information (CSI) is not available in real systems. Due to the high transmission rate and a high molecular absorption, spreading loss and reflection loss, the discrete-time channel impulse response (CIR) of the THz channel is very long and exhibits an approximately sparse characteristic. Conventional least-squares (LS) channel estimation does not incorporate the sparsity assumption into the estimation process. Therefore, in this work, sparse channel estimation for an indoor THz transmission example scenario using compressive sensing (CS) techniques is analyzed. A CS method based on solving a convex program by using the Dantzig selector (DS) and a CS approach using a greedy pursuit method called compressive sampling matching pursuit (CoSaMP) are investigated. All methods are analyzed with respect to mean squared error (MSE) performance, computational efficiency and numbers of observations needed. The numerical results show that significant advantages over LS estimation are achievable in all categories.