490 research outputs found
Generalized Approximate Message Passing for Massive MIMO mmWave Channel Estimation with Laplacian Prior
This paper tackles the problem of millimeter-Wave (mmWave) channel estimation
in massive MIMO communication systems. A new Bayes-optimal channel estimator is
derived using recent advances in the approximate belief propagation (BP)
Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave
MIMO channel in the angular domain, we recast the underlying channel estimation
problem into that of reconstructing a compressible signal from a set of noisy
linear measurements. Then, the generalized approximate message passing (GAMP)
algorithm is used to find the entries of the unknown mmWave MIMO channel
matrix. Unlike all the existing works on the same topic, we model the
angular-domain channel coefficients by Laplacian distributed random variables.
Further, we establish the closed-form expressions for the various statistical
quantities that need to be updated iteratively by GAMP. To render the proposed
algorithm fully automated, we also develop an expectation-maximization (EM)
based procedure that can be easily embedded within GAMP's iteration loop in
order to learn all the unknown parameters of the underlying Bayesian inference
problem. Computer simulations show that the proposed combined EM-GAMP algorithm
under a Laplacian prior exhibits improvements both in terms of channel
estimation accuracy, achievable rate, and computational complexity as compared
to the Gaussian mixture prior that has been advocated in the recent literature.
In addition, it is found that the Laplacian prior speeds up the convergence
time of GAMP over the entire signal-to-noise ratio (SNR) range.Comment: 15 pages, 5 figures, Published in IEEE Transactions on Communication
Low-Complexity Message Passing Based Massive MIMO Channel Estimation by Exploiting Unknown Sparse Common Support with Dirichlet Process
This paper investigates the problem of estimating sparse channels in massive
MIMO systems. Most wireless channels are sparse with large delay spread, while
some channels can be observed having sparse common support (SCS) within a
certain area of the antenna array, i.e., the antenna array can be grouped into
several clusters according to the sparse supports of channels. The SCS property
is attractive when it comes to the estimation of large number of channels in
massive MIMO systems. Using the SCS of channels, one expects better
performance, but the number of clusters and the elements for each cluster are
always unknown in the receiver. In this paper, {the Dirichlet process} is
exploited to model such sparse channels where those in each cluster have SCS.
We proposed a low complexity message passing based sparse Bayesian learning to
perform channel estimation in massive MIMO systems by using combined BP with MF
on a factor graph. Simulation results demonstrate that the proposed massive
MIMO sparse channel estimation outperforms the state-of-the-art algorithms.
Especially, it even shows better performance than the variational Bayesian
method applied for massive MIMO channel estimation.Comment: arXiv admin note: text overlap with arXiv:1409.4671 by other author
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
Time-Varying Downlink Channel Tracking for Quantized Massive MIMO Networks
This paper proposes a Bayesian downlink channel estimation algorithm for
time-varying massive MIMO networks. In particular, the quantization effects at
the receiver are considered. In order to fully exploit the sparsity and time
correlations of channels, we formulate the time-varying massive MIMO channel as
the simultaneously sparse signal model. Then, we propose a sparse Bayesian
learning (SBL) framework to learn the model parameters of the sparse virtual
channel. To reduce complexity, we employ the expectation maximization (EM)
algorithm to achieve the approximated solution. Specifically, the factor graph
and the general approximate message passing (GAMP) algorithms are used to
compute the desired posterior statistics in the expectation step, so that
high-dimensional integrals over the marginal distributions can be avoided. The
non-zero supporting vector of a virtual channel is then obtained from channel
statistics by a k-means clustering algorithm. After that, the reduced
dimensional GAMP based scheme is applied to make the full use of the channel
temporal correlation so as to enhance the virtual channel tracking accuracy.
Finally, we demonstrate the efficacy of the proposed schemes through
simulations.Comment: 30 Pages, 11 figure
An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems
We consider the problem of peak-to-average power ratio (PAPR) reduction in
orthogonal frequency-division multiplexing (OFDM) based massive multiple-input
multiple-output (MIMO) downlink systems. Specifically, given a set of symbol
vectors to be transmitted to K users, the problem is to find an OFDM-modulated
signal that has a low PAPR and meanwhile enables multiuser interference (MUI)
cancellation. Unlike previous works that tackled the problem using convex
optimization, we take a Bayesian approach and develop an efficient PAPR
reduction method by exploiting the redundant degrees-of-freedom of the transmit
array. The sought-after signal is treated as a random vector with a
hierarchical truncated Gaussian mixture prior, which has the potential to
encourage a low PAPR signal with most of its samples concentrated on the
boundaries. A variational expectation-maximization (EM) strategy is developed
to obtain estimates of the hyperparameters associated with the prior model,
along with the signal. In addition, the generalized approximate message passing
(GAMP) is embedded into the variational EM framework, which results in a
significant reduction in computational complexity of the proposed algorithm.
Simulation results show our proposed algorithm achieves a substantial
performance improvement over existing methods in terms of both the PAPR
reduction and computational complexity
Beamspace Channel Estimation in mmWave Systems via Cosparse Image Reconstruction Technique
This paper considers the beamspace channel estimation problem in 3D lens
antenna array under a millimeter-wave communication system. We analyze the
focusing capability of the 3D lens antenna array and the sparsity of the
beamspace channel response matrix. Considering the analysis, we observe that
the channel matrix can be treated as a 2D natural image; that is, the channel
is sparse, and the changes between adjacent elements are subtle. Thus, for the
channel estimation, we incorporate an image reconstruction technique called
sparse non-informative parameter estimator-based cosparse analysis AMP for
imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate
than earlier algorithms such as orthogonal matching pursuit and support
detection algorithms. To further improve the SCAMPI algorithm, we model the
channel distribution as a generic Gaussian mixture (GM) probability and embed
the expectation maximization learning algorithm into the SCAMPI algorithm to
learn the parameters in the GM probability. We show that the GM probability
outperforms the common uniform distribution used in image reconstruction. We
also propose a phase-shifter-reduced selection network structure to decrease
the power consumption of the system and prove that the SCAMPI algorithm is
robust even if the number of phase shifters is reduced by 10%
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
Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs
We propose a fast and near-optimal approach to joint channel-estimation,
equalization, and decoding of coded single-carrier (SC) transmissions over
frequency-selective channels with few-bit analog-to-digital converters (ADCs).
Our approach leverages parametric bilinear generalized approximate message
passing (PBiGAMP) to reduce the implementation complexity of joint channel
estimation and (soft) symbol decoding to that of a few fast Fourier transforms
(FFTs). Furthermore, it learns and exploits sparsity in the channel impulse
response. Our work is motivated by millimeter-wave systems with bandwidths on
the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast
processing all lead to significant reductions in power consumption and
implementation cost. We numerically demonstrate our approach using signals and
channels generated according to the IEEE 802.11ad wireless local area network
(LAN) standard, in the case that the receiver uses analog beamforming and a
single ADC
Deep Learning-Based Channel Estimation for High-Dimensional Signals
We propose a novel deep learning-based channel estimation technique for
high-dimensional communication signals that does not require any training. Our
method is broadly applicable to channel estimation for multicarrier signals
with any number of antennas, and has low enough complexity to be used in a
mobile station. The proposed deep channel estimator can outperform LS
estimation with nearly the same complexity, and approach MMSE estimation
performance to within 1 dB without knowing the second order statistics. The
only complexity increase with respect to LS estimator lies in fitting the
parameters of a deep neural network (DNN) periodically on the order of the
channel coherence time. We empirically show that the main benefit of this
method accrues from the ability of this specially designed DNN to exploit
correlations in the time-frequency grid. The proposed estimator can also reduce
the number of pilot tones needed in an OFDM time-frequency grid, e.g. in an LTE
scenario by 98% (68%) when the channel coherence time interval is 73ms (4.5ms)
Compressive Sensing with Prior Support Quality Information and Application to Massive MIMO Channel Estimation with Temporal Correlation
In this paper, we consider the problem of compressive sensing (CS) recovery
with a prior support and the prior support quality information available.
Different from classical works which exploit prior support blindly, we shall
propose novel CS recovery algorithms to exploit the prior support adaptively
based on the quality information. We analyze the distortion bound of the
recovered signal from the proposed algorithm and we show that a better quality
prior support can lead to better CS recovery performance. We also show that the
proposed algorithm would converge in \mathcal{O}\left(\log\mbox{SNR}\right)
steps. To tolerate possible model mismatch, we further propose some robustness
designs to combat incorrect prior support quality information. Finally, we
apply the proposed framework to sparse channel estimation in massive MIMO
systems with temporal correlation to further reduce the required pilot training
overhead.Comment: 14 double-column pages, accepted for publication in IEEE transactions
on signal processing in May, 201
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