989 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
Intelligent Surface-Aided Transmitter Architectures for Millimeter Wave Ultra Massive MIMO Systems
In this paper, we study two novel massive multiple-input multiple-output
(MIMO) transmitter architectures for millimeter wave (mmWave) communications
which comprise few active antennas, each equipped with a dedicated radio
frequency (RF) chain, that illuminate a nearby large intelligent
reflecting/transmitting surface (IRS/ITS). The IRS (ITS) consists of a large
number of low-cost and energy-efficient passive antenna elements which are able
to reflect (transmit) a phase-shifted version of the incident electromagnetic
field. Similar to lens array (LA) antennas, IRS/ITS-aided antenna architectures
are energy efficient due to the almost lossless over-the-air connection between
the active antennas and the intelligent surface. However, unlike for LA
antennas, for which the number of active antennas has to linearly grow with the
number of passive elements (i.e., the lens aperture) due to the
non-reconfigurablility (i.e., non-intelligence) of the lens, for IRS/ITS-aided
antennas, the reconfigurablility of the IRS/ITS facilitates scaling up the
number of radiating passive elements without increasing the number of costly
and bulky active antennas. We show that the constraints that the precoders for
IRS/ITS-aided antennas have to meet differ from those of conventional MIMO
architectures. Taking these constraints into account and exploiting the
sparsity of mmWave channels, we design two efficient precoders; one based on
maximizing the mutual information and one based on approximating the optimal
unconstrained fully digital (FD) precoder via the orthogonal matching pursuit
algorithm. Furthermore, we develop a power consumption model for IRS/ITS-aided
antennas that takes into account the impacts of the IRS/ITS imperfections,
namely the spillover loss, taper loss, aperture loss, and phase shifter loss.Comment: Journal version of arXiv:1811.0294
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink
multi-user communication from a multi-antenna base station is investigated in
this paper. We develop energy-efficient designs for both the transmit power
allocation and the phase shifts of the surface reflecting elements, subject to
individual link budget guarantees for the mobile users. This leads to
non-convex design optimization problems for which to tackle we propose two
computationally affordable approaches, capitalizing on alternating
maximization, gradient descent search, and sequential fractional programming.
Specifically, one algorithm employs gradient descent for obtaining the RIS
phase coefficients, and fractional programming for optimal transmit power
allocation. Instead, the second algorithm employs sequential fractional
programming for the optimization of the RIS phase shifts. In addition, a
realistic power consumption model for RIS-based systems is presented, and the
performance of the proposed methods is analyzed in a realistic outdoor
environment. In particular, our results show that the proposed RIS-based
resource allocation methods are able to provide up to higher energy
efficiency, in comparison with the use of regular multi-antenna
amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included
in the 2018 conference publications at ICASSP
(https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018
(arXiv:1809.05397
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