  The near-field effect of short-range multiple-input multiple-output (MIMO)
systems imposes many challenges on direction-of-arrival (DoA) estimation. Most
conventional scenarios assume that the far-field planar wavefronts hold. In
this paper, we investigate the DoA estimation problem in short-range MIMO
communications, where the effect of near-field spherical wave is
non-negligible. By converting it into a regression task, a novel DoA estimation
framework based on complex-valued deep learning (CVDL) is proposed for the
near-field region in short-range MIMO communication systems. Under the
assumption of a spherical wave model, the array steering vector is determined
by both the distance and the direction. However, solving this regression task
containing a massive number of variables is challenging, since datasets need to
capture numerous complicated feature representations. To overcome this, a
virtual covariance matrix (VCM) based on received signals is constructed, and
thus such features extracted from the VCM can deal with the complicated
coupling relationship between the direction and the distance. Although the
emergence of wireless big data driven by future communication networks promotes
deep learning-based wireless signal processing, the learning algorithms of
complex-valued signals are still ongoing. This paper proposes a one-dimensional
(1-D) residual network that can directly tackle complex-valued features due to
the inherent 1-D structure of signal subspace vectors. In addition, we put
forth a cropped VCM based policy which can be applied to different antenna
sizes. The proposed method is able to fully exploit the complex-valued
information. Our simulation results demonstrate the superiority of the proposed
CVDL approach over the baseline schemes in terms of the accuracy of DoA
estimation.
