609 research outputs found
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
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
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
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
Reliable OFDM Receiver with Ultra-Low Resolution ADC
The use of low-resolution analog-to-digital converters (ADCs) can
significantly reduce power consumption and hardware cost. However, their
resulting severe nonlinear distortion makes reliable data transmission
challenging. For orthogonal frequency division multiplexing (OFDM)
transmission, the orthogonality among subcarriers is destroyed. This
invalidates conventional OFDM receivers relying heavily on this orthogonality.
In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation
based on our previous achievement in optimal Q-OFDM detection. First, we
propose a novel Q-OFDM channel estimator by extending the generalized Turbo
(GTurbo) framework formerly applied for optimal detection. Specifically, we
integrate a type of robust linear OFDM channel estimator into the original
GTurbo framework and derive its corresponding extrinsic information to
guarantee its convergence. We also propose feasible schemes for automatic gain
control, noise power estimation, and synchronization. Combined with the
proposed inference algorithms, we develop an efficient Q-OFDM receiver
architecture. Furthermore, we construct a proof-of-concept prototyping system
and conduct over-the-air (OTA) experiments to examine its feasibility and
reliability. This is the first work that focuses on both algorithm design and
system implementation in the field of low-resolution quantization
communication. The results of the numerical simulation and OTA experiment
demonstrate that reliable data transmission can be achieved.Comment: 14 pages, 17 figures; accepted by IEEE Transactions on Communication
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
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
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
Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications
We investigate a general channel estimation problem in the massive
multiple-input multiple-output (MIMO) system which employs the hybrid
analog/digital precoding structure with limited radio-frequency (RF) chains. By
properly designing RF combiners and performing multiple trainings, the proposed
channel estimation can approach the performance of fully-digital estimations
depending on the degree of channel spatial correlation and the number of RF
chains. Dealing with the hybrid channel estimation, the optimal combiner is
theoretically derived by relaxing the constant-magnitude constraint in a
specific single-training scenario, which is then extended to the design of
combiners for multiple trainings by Sequential and Alternating methods.
Further, we develop a technique to generate the phase-only RF combiners based
on the corresponding unconstrained ones to satisfy the constant-magnitude
constraints. The performance of the proposed hybrid channel estimation scheme
is examined by simulations under both nonparametric and spatial channel models.
The simulation results demonstrate that the estimated CSI can approach the
performance of fully-digital estimations in terms of both mean square error and
spectral efficiency. Moreover, a practical spatial channel covariance
estimation method is proposed and its effectiveness in hybrid channel
estimation is verified by simulations
Channel Acquisition for Massive MIMO-OFDM with Adjustable Phase Shift Pilots
We propose adjustable phase shift pilots (APSPs) for channel acquisition in
wideband massive multiple-input multiple-output (MIMO) systems employing
orthogonal frequency division multiplexing (OFDM) to reduce the pilot overhead.
Based on a physically motivated channel model, we first establish a
relationship between channel space-frequency correlations and the channel power
angle-delay spectrum in the massive antenna array regime, which reveals the
channel sparsity in massive MIMO-OFDM. With this channel model, we then
investigate channel acquisition, including channel estimation and channel
prediction, for massive MIMO-OFDM with APSPs. We show that channel acquisition
performance in terms of sum mean square error can be minimized if the user
terminals' channel power distributions in the angle-delay domain can be made
non-overlapping with proper phase shift scheduling. A simplified pilot phase
shift scheduling algorithm is developed based on this optimal channel
acquisition condition. The performance of APSPs is investigated for both one
symbol and multiple symbol data models. Simulations demonstrate that the
proposed APSP approach can provide substantial performance gains in terms of
achievable spectral efficiency over the conventional phase shift orthogonal
pilot approach in typical mobility scenarios.Comment: 15 pages, 4 figures, accepted for publication in the IEEE
Transactions on Signal Processin
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