Channel Estimation Based Intelligent Reflecting Surfaces for Massive MIMO System Considering Spatially Correlated Channels

Recently, many fundamental technologies have emerged to boost and improve the performance of existing and future wireless communication systems, one of these technologies being the utilization of intelligent reflecting surfaces (IRS). This work investigates the channel estimation and spectral efficiency (SE) of a Massive multiple-input multiple-output (M-MIMO) system based on an IRS for spatially correlated channels. The system's performance is evaluated in terms of both channel estimation and SE, utilizing the minimum mean square error (MMSE) estimator. Accordingly, a three-stage M-MIMO channel estimation assisted by an IRS using the pilot sequences in a more practical propagation environment, that is, spatially correlated channels, wherein the IRS components empower the BS to estimate the uplink reflected channel state information (CSI) (i.e., estimation of reflected channels). In addition, the three stages channel estimate based on pilot sequences is computed and evaluated using the MMSE estimator and the normalized-mean square error (NMSE) metric, respectively. In this framework, this work proposes a local multiple scattering (LMS) model that describes the spatial correlation (SC) over the proposed uniform rectangular array (URA) by relying on the LMS model that describes the SC over a ULA configuration. In other words, using the Kronecker product (KP) of the correlation matrix constructed through a ULA, we built the correlation matrix that describes the SC over the proposed URA. In contrast to the linear array, the proposed array design is more constrained, leading to a higher degree of SC and better channel estimation quality. Numerical results are provided to assert and validate both our theoretical expression, as well as, the effectiveness of the proposed configuration.