120 research outputs found
Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation
Compressed sensing has been employed to reduce the pilot overhead for channel
estimation in wireless communication systems. Particularly, structured turbo
compressed sensing (STCS) provides a generic framework for structured sparse
signal recovery with reduced computational complexity and storage requirement.
In this paper, we consider the problem of massive multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
channel estimation in a frequency division duplexing (FDD) downlink system. By
exploiting the structured sparsity in the angle-frequency domain (AFD) and
angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the
channel by using AFD and ADD probability models and design message-passing
based channel estimators under the STCS framework. Several STCS-based
algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting
the structured sparsity. We show that, compared with other existing algorithms,
the proposed algorithms have a much faster convergence speed and achieve
competitive error performance under a wide range of simulation settings.Comment: 29 pages, 9 figure
Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems
When there are a large number of antennas in massive MIMO systems, the
transmitted wideband signal will be sensitive to the physical propagation delay
of electromagnetic waves across the large array aperture, which is called the
spatial-wideband effect. In this scenario, transceiver design is different from
most of the existing works, which presume that the bandwidth of the transmitted
signals is not that wide, ignore the spatial-wideband effect, and only address
the frequency selectivity. In this paper, we investigate spatial- and
frequency-wideband effects, called dual-wideband effects, in massive MIMO
systems from array signal processing point of view. Taking mmWave-band
communications as an example, we describe the transmission process to address
the dual-wideband effects. By exploiting the channel sparsity in the angle
domain and the delay domain, we develop the efficient uplink and downlink
channel estimation strategies that require much less amount of training
overhead and cause no pilot contamination. Thanks to the array signal
processing techniques, the proposed channel estimation is suitable for both TDD
and FDD massive MIMO systems. Numerical examples demonstrate that the proposed
transmission design for massive MIMO systems can effectively deal with the
dual-wideband effects.Comment: 13 pages, 10 figures. Index terms: Massive MIMO, mmWave, array signal
processing, wideband, spatial-wideband, beam squint, angle reciprocity, delay
reciprocity. Submitted to IEEE Transactions on Signal Processin
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO
Orthogonal time frequency space (OTFS) modulation outperforms orthogonal
frequency division multiplexing (OFDM) in high-mobility scenarios. One
challenge for OTFS massive MIMO is downlink channel estimation due to the large
number of base station antennas. In this paper, we propose a 3D structured
orthogonal matching pursuit algorithm based channel estimation technique to
solve this problem. First, we show that the OTFS MIMO channel exhibits 3D
structured sparsity: normal sparsity along the delay dimension, block sparsity
along the Doppler dimension, and burst sparsity along the angle dimension.
Based on the 3D structured channel sparsity, we then formulate the downlink
channel estimation problem as a sparse signal recovery problem. Simulation
results show that the proposed algorithm can achieve accurate channel state
information with low pilot overhead
FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification
We propose a novel method for massive Multiple-Input Multiple-Output (massive
MIMO) in Frequency Division Duplexing (FDD) systems. Due to the large frequency
separation between Uplink (UL) and Downlink (DL), in FDD systems channel
reciprocity does not hold. Hence, in order to provide DL channel state
information to the Base Station (BS), closed-loop DL channel probing and
Channel State Information (CSI) feedback is needed. In massive MIMO this incurs
typically a large training overhead. For example, in a typical configuration
with M = 200 BS antennas and fading coherence block of T = 200 symbols, the
resulting rate penalty factor due to the DL training overhead, given by max{0,
1 - M/T}, is close to 0. To reduce this overhead, we build upon the well-known
fact that the Angular Scattering Function (ASF) of the user channels is
invariant over frequency intervals whose size is small with respect to the
carrier frequency (as in current FDD cellular standards). This allows to
estimate the users' DL channel covariance matrix from UL pilots without
additional overhead. Based on this covariance information, we propose a novel
sparsifying precoder in order to maximize the rank of the effective sparsified
channel matrix subject to the condition that each effective user channel has
sparsity not larger than some desired DL pilot dimension T_{dl}, resulting in
the DL training overhead factor max{0, 1 - T_{dl} / T} and CSI feedback cost of
T_{dl} pilot measurements. The optimization of the sparsifying precoder is
formulated as a Mixed Integer Linear Program, that can be efficiently solved.
Extensive simulation results demonstrate the superiority of the proposed
approach with respect to concurrent state-of-the-art schemes based on
compressed sensing or UL/DL dictionary learning.Comment: 30 pages, 7 figures - Further simulation results and comparisons with
the state-of-the-art techniques, compared to the previous versio
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
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
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
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
Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO with One-Dimensional Antenna Array
In this paper, we study blind channel-and-signal estimation by exploiting the
burst-sparse structure of angular-domain propagation channels in massive MIMO
systems. The state-of-the-art approach utilizes the structured channel sparsity
by sampling the angular-domain channel representation with a uniform
angle-sampling grid, a.k.a. virtual channel representation. However, this
approach is only applicable to uniform linear arrays and may cause a
substantial performance loss due to the mismatch between the virtual
representation and the true angle information. To tackle these challenges, we
propose a sparse channel representation with a super-resolution sampling grid
and a hidden Markovian support. Based on this, we develop a novel approximate
inference based blind estimation algorithm to estimate the channel and the user
signals simultaneously, with emphasis on the adoption of the
expectation-maximization method to learn the angle information. Furthermore, we
demonstrate the low-complexity implementation of our algorithm, making use of
factor graph and message passing principles to compute the marginal posteriors.
Numerical results show that our proposed method significantly reduces the
estimation error compared to the state-of-the-art approach under various
settings, which verifies the efficiency and robustness of our method.Comment: 16 pages, 10 figure
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