3 research outputs found
Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO
Millimeter-wave massive multiple-input-multiple-output (mmWave mMIMO) is
reported as a key enabler in the fifth-generation communication and beyond. It
is customary to use a lens antenna array to transform a mmWave mMIMO channel
into a beamspace where the channel exhibits sparsity. Exploiting this sparsity
enables the applicability of hybrid precoding and achieves pilot reduction.
This beamspace transformation is equivalent to performing a Fourier
transformation of the channel. A motivation for the Fourier character of this
transformation is the fact that the steering response vectors in antenna arrays
are Fourier basis vectors. Still, a Fourier transformation is not necessarily
the optimal one, due to many reasons. Accordingly, this paper proposes using a
learned sparsifying dictionary as the transformation operator leading to
another beamspace. Since the dictionary is obtained by training over actual
channel measurements, this transformation is shown to yield two immediate
advantages. First, is enhancing channel sparsity, thereby leading to more
efficient pilot reduction. Second, is improving the channel representation
quality, and thus reducing the underlying power leakage phenomenon.
Consequently, this allows for both improved channel estimation and facilitated
beam selection in mmWave mMIMO. This is especially the case when the antenna
array is not perfectly uniform. Besides, a learned dictionary is also used as
the precoding operator for the same reasons. Extensive simulations under
various operating scenarios and environments validate the added benefits of
using learned dictionaries in improving the channel estimation quality and the
beam selectivity, thereby improving the spectral efficiency.Comment: This work has been submitted to the IEEE for possible publication.
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