Beam Squint and Committee Machine-Based Channel Estimation Scheme for Wideband THz mMIMO-OFDM Systems

Terahertz (THz) communication has been envisioned as a promising technique for the future sixth generation (6G) and beyond wireless networks because of its tens of gigahertz (GHz) bandwidth. However, wideband THz channel results in an increase in bandwidth, which gives rise to the phenomenon known as beam squint. Additionally, techniques based on the standard multiple-input multiple-output (MIMO) paradigm, such as channel estimation (CE), are rendered inapplicable by beam squint. Several sparse CE algorithms have been proposed in compressed sensing (CS) to accurately estimate the wideband THz massive multiple-input-multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) channel. However, the exploitation of these expert algorithms constitutes a committee machine (CM), which is likely to be superior to that obtained by any one of the committee expert acting separately. In this paper, by leveraging the notion of CM methodology, we address the CE problem in wideband THz mMIMO-OFDM systems with beam squint. To estimate the wideband THz mMIMO sparse channel vectors efficiently, we first present a committee machine technique for CS (CMTCS), which makes use of the estimations from multiple expert algorithms. Next, in order to enhance the wideband THz mMIMO channel estimation performance, we develop the iterative CMTCS (ICMTCS) technique, a CMTCS algorithm extension. Using the restricted isometry property (RIP), the theoretical analysis of the proposed schemes for realizing an improved channel reconstruction performance is presented. Simulation results demonstrate that the proposed schemes are effective and offer better CE performance in terms of normalized mean squared-error (NMSE) than those dictated by other CS-based CE algorithms and the traditional least-squares-based methods.