31,392 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO
Location-aided beam alignment has been proposed recently as a potential
approach for fast link establishment in millimeter wave (mmWave) massive MIMO
(mMIMO) communications. However, due to mobility and other imperfections in the
estimation process, the spatial information obtained at the base station (BS)
and the user (UE) is likely to be noisy, degrading beam alignment performance.
In this paper, we introduce a robust beam alignment framework in order to
exhibit resilience with respect to this problem. We first recast beam alignment
as a decentralized coordination problem where BS and UE seek coordination on
the basis of correlated yet individual position information. We formulate the
optimum beam alignment solution as the solution of a Bayesian team decision
problem. We then propose a suite of algorithms to approach optimality with
reduced complexity. The effectiveness of the robust beam alignment procedure,
compared with classical designs, is then verified on simulation settings with
varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been
accepted to IEEE Globecom 201
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