  In this paper, we propose a deep learning (DL)-based approach for efficiently
computing the inverse of Hermitian matrices using truncated polynomial
expansion (TPE). Our model-driven approach involves optimizing the coefficients
of the TPE during an offline training procedure for a given number of TPE
terms. We apply this method to signal detection in uplink massive
multiple-input multiple-output (MIMO) systems, where the matrix inverse
operation required by linear detectors, such as zero-forcing (ZF) and minimum
mean square error (MMSE), is approximated using TPE. Our simulation results
demonstrate that the proposed learned TPE-based method outperforms the
conventional TPE method with optimal coefficients in terms of asymptotic
convergence speed and reduces the computational complexity of the online
detection stage, albeit at the expense of the offline training stage. However,
the limited number of trainable parameters leads to a swift offline training
process.
