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Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

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Document pages: 11 pages

Abstract: Millimeter-wave massive multiple-input-multiple-output (mmWave mMIMO) isreported as a key enabler in the fifth-generation communication and beyond. Itis customary to use a lens antenna array to transform a mmWave mMIMO channelinto a beamspace where the channel exhibits sparsity. Exploiting this sparsityenables the applicability of hybrid precoding and achieves pilot reduction.This beamspace transformation is equivalent to performing a Fouriertransformation of the channel. A motivation for the Fourier character of thistransformation is the fact that the steering response vectors in antenna arraysare Fourier basis vectors. Still, a Fourier transformation is not necessarilythe optimal one, due to many reasons. Accordingly, this paper proposes using alearned sparsifying dictionary as the transformation operator leading toanother beamspace. Since the dictionary is obtained by training over actualchannel measurements, this transformation is shown to yield two immediateadvantages. First, is enhancing channel sparsity, thereby leading to moreefficient pilot reduction. Second, is improving the channel representationquality, and thus reducing the underlying power leakage phenomenon.Consequently, this allows for both improved channel estimation and facilitatedbeam selection in mmWave mMIMO. This is especially the case when the antennaarray is not perfectly uniform. Besides, a learned dictionary is also used asthe precoding operator for the same reasons. Extensive simulations undervarious operating scenarios and environments validate the added benefits ofusing learned dictionaries in improving the channel estimation quality and thebeam selectivity, thereby improving the spectral efficiency.

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