Abstract
Numerous low-complexity iterative algorithms have been proposed to offer the
performance of linear multiple-input multiple-output (MIMO) detectors bypassing
the channel matrix inverse. These algorithms exhibit fast convergence in
well-conditioned MIMO channels. However, in the emerging MIMO paradigm
utilizing extremely large aperture arrays (ELAA), the wireless channel may
become ill-conditioned because of spatial non-stationarity, which results in a
considerably slower convergence rate for these algorithms. In this paper, we
propose a novel ELAA-MIMO detection scheme that leverages user-wise singular
value decomposition (UW-SVD) to accelerate the convergence of these iterative
algorithms. By applying UW-SVD, the MIMO signal model can be converted into an
equivalent form featuring a better-conditioned transfer function. Then,
existing iterative algorithms can be utilized to recover the transmitted signal
from the converted signal model with accelerated convergence towards
zero-forcing performance. Our simulation results indicate that proposed UW-SVD
scheme can significantly accelerate the convergence of the iterative algorithms
in spatially non-stationary ELAA channels. Moreover, the computational
complexity of the UW-SVD is comparatively minor in relation to the inherent
complexity of the iterative algorithms.