3,156 research outputs found
Online unsupervised deep unfolding for massive MIMO channel estimation
Massive MIMO communication systems have a huge potential both in terms of
data rate and energy efficiency, although channel estimation becomes
challenging for a large number antennas. Using a physical model allows to ease
the problem by injecting a priori information based on the physics of
propagation. However, such a model rests on simplifying assumptions and
requires to know precisely the configuration of the system, which is
unrealistic in practice. In this letter, we propose to perform online learning
for channel estimation in a massive MIMO context, adding flexibility to
physical channel models by unfolding a channel estimation algorithm (matching
pursuit) as a neural network. This leads to a computationally efficient neural
network structure that can be trained online when initialized with an imperfect
model. The method allows a base station to automatically correct its channel
estimation algorithm based on incoming data, without the need for a separate
offline training phase. It is applied to realistic millimeter wave channels and
shows great performance, achieving a channel estimation error almost as low as
one would get with a perfectly calibrated system
Robust Geometry-Based User Scheduling for Large MIMO Systems Under Realistic Channel Conditions
The problem of user scheduling with reduced overhead of channel estimation in
the uplink of Massive multiple-input multiple-output (MIMO) systems has been
considered. A geometry-based stochastic channel model (GSCM), called the COST
2100 channel model has been used for realistic analysis of channels. In this
paper, we propose a new user selection algorithm based on knowledge of the
geometry of the service area and location of clusters, without having full
channel state information (CSI) at the base station (BS). The multi-user link
correlation in the GSCMs arises from the common clusters in the area. The
throughput depends on the position of clusters in the GSCMs and users in the
system. Simulation results show that although the BS does not require the
channel information of all users, by the proposed geometry-based user
scheduling algorithm the sum-rate of the system is only slightly less than the
well-known greedy weight clique scheme. Finally, the robustness of the proposed
algorithm to the inaccuracy of cluster localization is verified by the
simulation results.Comment: 4 figure
High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches
Massive MIMO has been regarded as one of the key technologies for 5G wireless
networks, as it can significantly improve both the spectral efficiency and
energy efficiency. The availability of high-dimensional channel side
information (CSI) is critical for its promised performance gains, but the
overhead of acquiring CSI may potentially deplete the available radio
resources. Fortunately, it has recently been discovered that harnessing various
sparsity structures in massive MIMO channels can lead to significant overhead
reduction, and thus improve the system performance. This paper presents and
discusses the use of sparsity-inspired CSI acquisition techniques for massive
MIMO, as well as the underlying mathematical theory. Sparsity-inspired
approaches for both frequency-division duplexing and time-division duplexing
massive MIMO systems will be examined and compared from an overall system
perspective, including the design trade-offs between the two duplexing modes,
computational complexity of acquisition algorithms, and applicability of
sparsity structures. Meanwhile, some future prospects for research on
high-dimensional CSI acquisition to meet practical demands will be identified.Comment: 15 pages, 3 figures, 1 table, submitted to IEEE Systems Journal
Special Issue on 5G Wireless Systems with Massive MIM
Massive MIMO and Waveform Design for 5th Generation Wireless Communication Systems
This article reviews existing related work and identifies the main challenges
in the key 5G area at the intersection of waveform design and large-scale
multiple antenna systems, also known as Massive MIMO. The property of
self-equalization is introduced for Filter Bank Multicarrier (FBMC)-based
Massive MIMO, which can reduce the number of subcarriers required by the
system. It is also shown that the blind channel tracking property of FBMC can
be used to address pilot contamination -- one of the main limiting factors of
Massive MIMO systems. Our findings shed light into and motivate for an entirely
new research line towards a better understanding of waveform design with
emphasis on FBMC-based Massive MIMO networks.Comment: 6 pages, 2 figures, 1st International Conference on 5G for Ubiquitous
Connectivit
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint
Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication
is a key technology for next generation wireless networks. One of the
consequences of utilizing a large number of antennas with an increased
bandwidth is that array steering vectors vary among different subcarriers. Due
to this effect, known as beam squint, the conventional channel model is no
longer applicable for mmWave massive MIMO systems. In this paper, we study
channel estimation under the resulting non-standard model. To that aim, we
first analyze the beam squint effect from an array signal processing
perspective, resulting in a model which sheds light on the angle-delay sparsity
of mmWave transmission. We next design a compressive sensing based channel
estimation algorithm which utilizes the shift-invariant block-sparsity of this
channel model. The proposed algorithm jointly computes the off-grid angles, the
off-grid delays, and the complex gains of the multi-path channel. We show that
the newly proposed scheme reflects the mmWave channel more accurately and
results in improved performance compared to traditional approaches. We then
demonstrate how this approach can be applied to recover both the uplink as well
as the downlink channel in frequency division duplex (FDD) systems, by
exploiting the angle-delay reciprocity of mmWave channels
Downlink channel spatial covariance estimation in realistic FDD massive MIMO systems
The knowledge of the downlink (DL) channel spatial covariance matrix at the
BS is of fundamental importance for large-scale array systems operating in
frequency division duplexing (FDD) mode. In particular, this knowledge plays a
key role in the DL channel state information (CSI) acquisition. In the massive
MIMO regime, traditional schemes based on DL pilots are severely limited by the
covariance feedback and the DL training overhead. To overcome this problem,
many authors have proposed to obtain an estimate of the DL spatial covariance
based on uplink (UL) measurements. However, many of these approaches rely on
simple channel models, and they are difficult to extend to more complex models
that take into account important effects of propagation in 3D environments and
of dual-polarized antenna arrays. In this study we propose a novel technique
that takes into account the aforementioned effects, in compliance with the
requirements of modern 4G and 5G system designs. Numerical simulations show the
effectiveness of our approach.Comment: [v2] is the version accepted at GlobalSIP 2018. Only minor changes
mainly in the introductio
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Efficient Downlink Channel Probing and Uplink Feedback in FDD Massive MIMO Systems
Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of
multi-user MIMO in which the number of antennas at each Base Station (BS) is
very large and typically much larger than the number of users simultaneously
served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or
Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are
particularly desirable due to their implementation in current wireless networks
and their efficiency in situations with symmetric traffic and delay-sensitive
applications. However, implementing FDD massive MIMO systems is known to be
challenging since it imposes a large feedback overhead in the Uplink (UL) to
obtain channel state information for the Downlink (DL). In recent years, a
considerable amount of research is dedicated to developing methods to reduce
the feedback overhead in such systems. In this paper, we use the sparse spatial
scattering properties of the environment to achieve this goal. The idea is to
estimate the support of the continuous, frequency-invariant scattering function
from UL channel observations and use this estimate to obtain the support of the
DL channel vector via appropriate interpolation. We use the resulting support
estimate to design an efficient DL probing and UL feedback scheme in which the
feedback dimension scales proportionally with the sparsity order of DL channel
vectors. Since the sparsity order is much less than the number of BS antennas
in almost all practically relevant scenarios, our method incurs much less
feedback overhead compared with the currently proposed methods in the
literature, such as those based on compressed-sensing. We use numerical
simulations to assess the performance of our probing-feedback algorithm and
compare it with these methods.Comment: 24 pages, 10 figure
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Communication at millimeter wave (mmWave) frequencies is defining a new era
of wireless communication. The mmWave band offers higher bandwidth
communication channels versus those presently used in commercial wireless
systems. The applications of mmWave are immense: wireless local and personal
area networks in the unlicensed band, 5G cellular systems, not to mention
vehicular area networks, ad hoc networks, and wearables. Signal processing is
critical for enabling the next generation of mmWave communication. Due to the
use of large antenna arrays at the transmitter and receiver, combined with
radio frequency and mixed signal power constraints, new multiple-input
multiple-output (MIMO) communication signal processing techniques are needed.
Because of the wide bandwidths, low complexity transceiver algorithms become
important. There are opportunities to exploit techniques like compressed
sensing for channel estimation and beamforming. This article provides an
overview of signal processing challenges in mmWave wireless systems, with an
emphasis on those faced by using MIMO communication at higher carrier
frequencies.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
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