406 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
Optimal Data Detection in Large MIMO
Large multiple-input multiple-output (MIMO) appears in massive multi-user
MIMO and randomly-spread code-division multiple access (CDMA)-based wireless
systems. In order to cope with the excessively high complexity of optimal data
detection in such systems, a variety of efficient yet sub-optimal algorithms
have been proposed in the past. In this paper, we propose a data detection
algorithm that is computationally efficient and optimal in a sense that it is
able to achieve the same error-rate performance as the individually optimal
(IO) data detector under certain assumptions on the MIMO system matrix and
constellation alphabet. Our algorithm, which we refer to as LAMA (short for
large MIMO AMP), builds on complex-valued Bayesian approximate message passing
(AMP), which enables an exact analytical characterization of the performance
and complexity in the large-system limit via the state-evolution framework. We
derive optimality conditions for LAMA and investigate performance/complexity
trade-offs. As a byproduct of our analysis, we recover classical results of IO
data detection for randomly-spread CDMA. We furthermore provide practical ways
for LAMA to approach the theoretical performance limits in realistic,
finite-dimensional systems at low computational complexity
Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
On the time-varying channel estimation, the traditional downlink (DL) channel
restoration schemes usually require the reconstruction for the covariance of
downlink process noise vector, which is dependent on DL channel covariance
matrix (CCM). However, the acquisition of the CCM leads to unacceptable
overhead in massive MIMO systems. To tackle this problem, in this paper, we
propose a novel scheme for the DL channel tracking. First, with the help of
virtual channel representation (VCR), we build a dynamic uplink (UL) massive
MIMO channel model with the consideration of off-grid refinement. Then, a
coordinate-wise maximization based expectation maximization (EM) algorithm is
adopted for capturing the model parameters, including the spatial signatures,
the time-correlation factors, the off-grid bias, the channel power, and the
noise power. Thanks to the angle reciprocity, the spatial signatures,
timecorrelation factors and off-grid bias of the DL channel model can be
reconstructed with the knowledge of UL ones. However, the other two kinds of
model parameters are closely related with the carrier frequency, which cannot
be perfectly inferred from the UL ones. Instead of relearning the DL model
parameters with dedicated training, we resort to the optimal Bayesian Kalman
filter (OBKF) method to accurately track the DL channel with the partially
prior knowledge. At the same time, the model parameters will be gradually
restored. Specially, the factor-graph and the Metropolis Hastings MCMC are
utilized within the OBKF framework. Finally, numerical results are provided to
demonstrate the efficiency of our proposed scheme.Comment: 30 pages, 11 figure
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
Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC
Orthogonal frequency division multiplexing (OFDM) has been widely used in
communication systems operating in the millimeter wave (mmWave) band to combat
frequency-selective fading and achieve multi-Gbps transmissions, such as IEEE
802.15.3c and IEEE 802.11ad. For mmWave systems with ultra high sampling rate
requirements, the use of low-resolution analog-to-digital converters (ADCs)
(i.e., 1-3 bits) ensures an acceptable level of power consumption and system
costs. However, orthogonality among sub-channels in the OFDM system cannot be
maintained because of the severe non-linearity caused by low-resolution ADC,
which renders the design of data detector challenging. In this study, we
develop an efficient algorithm for optimal data detection in the mmWave OFDM
system with low-resolution ADCs. The analytical performance of the proposed
detector is derived and verified to achieve the fundamental limit of the
Bayesian optimal design. On the basis of the derived analytical expression, we
further propose a power allocation (PA) scheme that seeks to minimize the
average symbol error rate. In addition to the optimal data detector, we also
develop a feasible channel estimation method, which can provide high-quality
channel state information without significant pilot overhead. Simulation
results confirm the accuracy of our analysis and illustrate that the
performance of the proposed detector in conjunction with the proposed PA scheme
is close to the optimal performance of the OFDM system with infinite-resolution
ADC.Comment: 32 pages, 12 figures; accepted by IEEE JSAC special issue on
millimeter wave communications for future mobile network
Unifying Message Passing Algorithms Under the Framework of Constrained Bethe Free Energy Minimization
Variational message passing (VMP), belief propagation (BP) and expectation
propagation (EP) have found their wide applications in complex statistical
signal processing problems. In addition to viewing them as a class of
algorithms operating on graphical models, this paper unifies them under an
optimization framework, namely, Bethe free energy minimization with differently
and appropriately imposed constraints. This new perspective in terms of
constraint manipulation can offer additional insights on the connection between
different message passing algorithms and is valid for a generic statistical
model. It also founds a theoretical framework to systematically derive message
passing variants. Taking the sparse signal recovery (SSR) problem as an
example, a low-complexity EP variant can be obtained by simple constraint
reformulation, delivering better estimation performance with lower complexity
than the standard EP algorithm. Furthermore, we can resort to the framework for
the systematic derivation of hybrid message passing for complex inference
tasks. Notably, a hybrid message passing algorithm is exemplarily derived for
joint SSR and statistical model learning with near-optimal inference
performance and scalable complexity
Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing
In this letter, a binary sparse Bayesian learning (BSBL) algorithm is
proposed to slove the one-bit compressed sensing (CS) problem in both single
measurement vector (SMV) and multiple measurement vectors (MMVs). By utilising
the Bussgang-like decomposition, the one-bit CS problem can be approximated as
a standard linear model. Consequently, the standard SBL algorithm can be
naturally incorporated. Numerical results demonstrate the effectiveness of the
BSBL algorithm
Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)
We study the problem of downlink channel estimation in multi-user massive
multiple input multiple output (MIMO) systems. To this end, we consider a
Bayesian compressive sensing approach in which the clustered sparse structure
of the channel in the angular domain is employed to reduce the pilot overhead.
To capture the clustered structure, we employ a conditionally independent
identically distributed Bernoulli-Gaussian prior on the sparse vector
representing the channel, and a Markov prior on its support vector. An
expectation propagation (EP) algorithm is developed to approximate the
intractable joint distribution on the sparse vector and its support with a
distribution from an exponential family. The approximate distribution is then
used for direct estimation of the channel. The EP algorithm assumes that the
model parameters are known a priori. Since these parameters are unknown, we
estimate these parameters using the expectation maximization (EM) algorithm.
The combination of EM and EP referred to as EM-EP algorithm is reminiscent of
the variational EM approach. Simulation results show that the proposed EM-EP
algorithm outperforms several recently-proposed algorithms in the literature
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
Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications
The massiveness of devices in crowded Machine-to-Machine (M2M) communications
brings new challenges to existing random-access (RA) schemes, such as heavy
signaling overhead and severe access collisions. In order to reduce the
signaling overhead, we propose a fixed-symbol aided RA scheme where active
devices access the network in a grant-free method, i.e., data packets are
directly transmitted in randomly chosen slots. To further address the access
collision which impedes the activity detection, one fixed symbol is inserted
into each transmitted data packet in the proposed scheme. An iterative message
passing based activity detection (MP-AD) algorithm is performed upon the
received signal of this fixed symbol to detect the device activity in each
slot. In addition, the deep neural network-aided MP-AD (DNN-MP-AD) algorithm is
further designed to alleviate the correlation problem of the iterative message
passing process. In the DNN-MP-AD algorithm, the iterative message passing
process is transferred from a factor graph to a DNN. Weights are imposed on the
messages in the DNN and further trained to improve the accuracy of the device
activity detection. Finally, numerical simulations are provided for the
throughput of the proposed RA scheme, the accuracy of the proposed MP-AD
algorithm, as well as the improvement brought by the DNN-MP-AD algorithm.Comment: 15 pages, 9 figure
- …