89 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
Frequency Spreading Equalization in Multicarrier Massive MIMO
Application of filter bank multicarrier (FBMC) as an effective method for
signaling over massive MIMO channels has been recently proposed. This paper
further expands the application of FBMC to massive MIMO by applying frequency
spreading equalization (FSE) to these channels. FSE allows us to achieve a more
accurate equalization. Hence, higher number of bits per symbol can be
transmitted and the bandwidth of each subcarrier can be widened. Widening the
bandwidth of each subcarrier leads to (i) higher bandwidth efficiency; (ii)
lower complexity; (iii) lower sensitivity to carrier frequency offset (CFO);
(iv) reduced peak-to-average power ratio (PAPR); and (iv) reduced latency. All
these appealing advantages have a direct impact on the digital as well as
analog circuitry that is needed for the system implementation. In this paper,
we develop the mathematical formulation of the minimum mean square error (MMSE)
FSE for massive MIMO systems. This analysis guides us to decide on the number
of subcarriers that will be sufficient for practical channel models.Comment: Accepted in IEEE ICC 2015 - Workshop on 5G & Beyond - Enabling
Technologies and Application
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination
We address the problem of noise and interference corrupted channel estimation
in massive MIMO systems. Interference, which originates from pilot reuse (or
contamination), can in principle be discriminated on the basis of the
distributions of path angles and amplitudes. In this paper we propose novel
robust channel estimation algorithms exploiting path diversity in both angle
and power domains, relying on a suitable combination of the spatial filtering
and amplitude based projection. The proposed approaches are able to cope with a
wide range of system and topology scenarios, including those where, unlike in
previous works, interference channel may overlap with desired channels in terms
of multipath angles of arrival or exceed them in terms of received power. In
particular we establish analytically the conditions under which the proposed
channel estimator is fully decontaminated. Simulation results confirm the
overall system gains when using the new methods.Comment: 14 pages, 5 figures, accepted for publication in IEEE Transactions on
Signal Processin
Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping
This paper tackles the problem of downlink transmission in massive
multiple-input multiple-output(MIMO) systems where the equipments (UEs) exhibit
high spatial correlation and the channel estimation is limited by strong pilot
contamination. Signal subspace separation among the UEs is, in fact, rarely
realized in practice and is generally beyond the control of the network
designer (as it is dictated by the physical scattering environment). In this
context, we propose a novel statistical beamforming technique, referred to
asMIMO covariance shaping, that exploits multiple antennas at the UEs and
leverages the realistic non-Kronecker structure of massive MIMO channels to
target a suitable shaping of the channel statistics performed at the UE-side.
To optimize the covariance shaping strategies, we propose a low-complexity
block coordinate descent algorithm that is proved to converge to a limit point
of the original nonconvex problem. For the two-UE case, this is shown to
converge to a stationary point of the original problem. Numerical results
illustrate the sum-rate performance gains of the proposed method with respect
to reference scenarios employing the multiple antennas at the UE for spatial
multiplexing.Comment: Submitted for journal publicatio
Channel estimation in massive MIMO systems
Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference.
The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity.
This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes.
System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array
gain and spatial multiplexing offered by Massive MIMO. Since its inception, the
coherent interference caused by pilot contamination has been believed to create
a finite capacity limit, as the number of antennas goes to infinity. In this
paper, we prove that this is incorrect and an artifact from using simplistic
channel models and suboptimal precoding/combining schemes. We show that with
multicell MMSE precoding/combining and a tiny amount of spatial channel
correlation or large-scale fading variations over the array, the capacity
increases without bound as the number of antennas increases, even under pilot
contamination. More precisely, the result holds when the channel covariance
matrices of the contaminating users are asymptotically linearly independent,
which is generally the case. If also the diagonals of the covariance matrices
are linearly independent, it is sufficient to know these diagonals (and not the
full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages,
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