1,262 research outputs found
Sparse Subspace Decomposition for Millimeter Wave MIMO Channel Estimation
Millimeter wave multiple-input multiple-output (MIMO) communication systems
must operate over sparse wireless links and will require large antenna arrays
to provide high throughput. To achieve sufficient array gains, these systems
must learn and adapt to the channel state conditions. However, conventional
MIMO channel estimation can not be directly extended to millimeter wave due to
the constraints on cost-effective millimeter wave operation imposed on the
number of available RF chains. Sparse subspace scanning techniques that search
for the best subspace sample from the sounded subspace samples have been
investigated for channel estimation.However, the performance of these
techniques starts to deteriorate as the array size grows, especially for the
hybrid precoding architecture. The millimeter wave channel estimation challenge
still remains and should be properly addressed before the system can be
deployed and used to its full potential. In this work, we propose a sparse
subspace decomposition (SSD) technique for sparse millimeter wave MIMO channel
estimation. We formulate the channel estimation as an optimization problem that
minimizes the subspace distance from the received subspace samples. Alternating
optimization techniques are devised to tractably handle the non-convex problem.
Numerical simulations demonstrate that the proposed method outperforms other
existing techniques with remarkably low overhead
Subspace Estimation and Decomposition for Large Millimeter-Wave MIMO systems
Channel estimation and precoding in hybrid analog-digital millimeter-wave
(mmWave) MIMO systems is a fundamental problem that has yet to be addressed,
before any of the promised gains can be harnessed. For that matter, we propose
a method (based on the well-known Arnoldi iteration) exploiting channel
reciprocity in TDD systems and the sparsity of the channel's eigenmodes, to
estimate the right (resp. left) singular subspaces of the channel, at the BS
(resp. MS). We first describe the algorithm in the context of conventional MIMO
systems, and derive bounds on the estimation error in the presence of
distortions at both BS and MS. We later identify obstacles that hinder the
application of such an algorithm to the hybrid analog-digital architecture, and
address them individually. In view of fulfilling the constraints imposed by the
hybrid analog-digital architecture, we further propose an iterative algorithm
for subspace decomposition, whereby the above estimated subspaces, are
approximated by a cascade of analog and digital precoder / combiner. Finally,
we evaluate the performance of our scheme against the perfect CSI, fully
digital case (i.e., an equivalent conventional MIMO system), and conclude that
similar performance can be achieved, especially at medium-to-high SNR (where
the performance gap is less than 5%), however, with a drastically lower number
of RF chains (4 to 8 times less).Comment: journal, 13 page
Low-Complexity Statistically Robust Precoder/Detector Computation for Massive MIMO Systems
Massive MIMO is a variant of multiuser MIMO in which the number of antennas
at the base station (BS) is very large and typically much larger than the
number of served users (data streams) . Recent research has illustrated the
system-level advantages of such a system and in particular the beneficial
effect of increasing the number of antennas . These benefits, however, come
at the cost of dramatic increase in hardware and computational complexity. This
is partly due to the fact that the BS needs to compute suitable beamforming
vectors in order to coherently transmit/receive data to/from each user, where
the resulting complexity grows proportionally to the number of antennas and
the number of served users . Recently, different algorithms based on tools
from random matrix theory in the asymptotic regime of with
have been proposed to reduce such complexity.
The underlying assumption in all these techniques, however, is that the exact
statistics (covariance matrix) of the channel vectors of the users is a priori
known. This is far from being realistic, especially that in the high-dim regime
of , estimation of the underlying covariance matrices is well
known to be a very challenging problem.
In this paper, we propose a novel technique for designing beamforming vectors
in a massive MIMO system. Our method is based on the randomized Kaczmarz
algorithm and does not require knowledge of the statistics of the users channel
vectors. We analyze the performance of our proposed algorithm theoretically and
compare its performance with that of other competitive techniques via numerical
simulations. Our results indicate that our proposed technique has a comparable
performance while it does not require the knowledge of the statistics of the
users channel vectors.Comment: to appear in \textit{IEEE Transactions on Wireless Communications
FDD massive MIMO channel spatial covariance conversion using projection methods
Knowledge of second-order statistics of channels (e.g. in the form of
covariance matrices) is crucial for the acquisition of downlink channel state
information (CSI) in massive MIMO systems operating in the frequency division
duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance
information via feedback of the estimated covariance matrix from the user
equipment (UE), but in the massive MIMO regime this approach is infeasible
because of the unacceptably high training overhead. This paper considers
instead the problem of estimating the downlink channel covariance from uplink
measurements. We propose two variants of an algorithm based on projection
methods in an infinite-dimensional Hilbert space that exploit channel
reciprocity properties in the angular domain. The proposed schemes are
evaluated via Monte Carlo simulations, and they are shown to outperform current
state-of-the art solutions in terms of accuracy and complexity, for typical
array geometries and duplex gaps.Comment: Paper accepted on 29/01/2018 for presentation at ICASSP 201
Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO
Massive MIMO is a promising technique for future 5G communications due to its
high spectrum and energy efficiency. To realize its potential performance gain,
accurate channel estimation is essential. However, due to massive number of
antennas at the base station (BS), the pilot overhead required by conventional
channel estimation schemes will be unaffordable, especially for frequency
division duplex (FDD) massive MIMO. To overcome this problem, we propose a
structured compressive sensing (SCS)-based spatio-temporal joint channel
estimation scheme to reduce the required pilot overhead, whereby the
spatio-temporal common sparsity of delay-domain MIMO channels is leveraged.
Particularly, we first propose the non-orthogonal pilots at the BS under the
framework of CS theory to reduce the required pilot overhead. Then, an adaptive
structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly
estimate channels associated with multiple OFDM symbols from the limited number
of pilots, whereby the spatio-temporal common sparsity of MIMO channels is
exploited to improve the channel estimation accuracy. Moreover, by exploiting
the temporal channel correlation, we propose a space-time adaptive pilot scheme
to further reduce the pilot overhead. Additionally, we discuss the proposed
channel estimation scheme in multi-cell scenario. Simulation results
demonstrate that the proposed scheme can accurately estimate channels with the
reduced pilot overhead, and it is capable of approaching the optimal oracle
least squares estimator.Comment: 16 pages; 12 figures;submitted to IEEE Trans. Communication
Low-Complexity Robust Adaptive Beamforming Algorithms Based on Shrinkage for Mismatch Estimation
In this paper, we propose low-complexity robust adaptive beamforming (RAB)
techniques that based on shrinkage methods. The only prior knowledge required
by the proposed algorithms are the angular sector in which the actual steering
vector is located and the antenna array geometry. We firstly present a
Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm to
estimate the desired signal steering vector mismatch, in which the
interference-plus-noise covariance (INC) matrix is estimated with Oracle
Approximating Shrinkage (OAS) method and the weights are computed with matrix
inversions. We then develop low-cost stochastic gradient (SG) recursions to
estimate the INC matrix and update the beamforming weights, resulting in the
proposed LOCSME-SG algorithm. Simulation results show that both LOCSME and
LOCSME-SG achieve very good output signal-to-interference-plus-noise ratio
(SINR) compared to previously reported adaptive RAB algorithms.Comment: 8 pages, 2 figures, WSA. arXiv admin note: text overlap with
arXiv:1311.233
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Hybrid massive MIMO structures with lower hardware complexity and power
consumption have been considered as a potential candidate for millimeter wave
(mmWave) communications. Channel covariance information can be used for
designing transmitter precoders, receiver combiners, channel estimators, etc.
However, hybrid structures allow only a lower-dimensional signal to be
observed, which adds difficulties for channel covariance matrix estimation. In
this paper, we formulate the channel covariance estimation as a structured
low-rank matrix sensing problem via Kronecker product expansion and use a
low-complexity algorithm to solve this problem. Numerical results with uniform
linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to
demonstrate the effectiveness of our proposed method
Millimeter-Wave Beamformed Full-dimensional MIMO Channel Estimation Based on Atomic Norm Minimization
The millimeter-wave (mmWave) full-dimensional (FD) MIMO system employs planar
arrays at both the base station and user equipment and can simultaneously
support both azimuth and elevation beamforming. In this paper, we propose
atomic-norm-based methods for mmWave FD-MIMO channel estimation under both
uniform planar arrays (UPA) and non-uniform planar arrays (NUPA). Unlike
existing algorithms such as compressive sensing (CS) or subspace methods, the
atomic-norm-based algorithms do not require to discretize the angle spaces of
the angle of arrival (AoA) and angle of departure (AoD) into grids, thus
provide much better accuracy in estimation. In the UPA case, to reduce the
computational complexity, the original large-scale 4D atomic norm minimization
problem is approximately reformulated as a semi-definite program (SDP)
containing two decoupled two-level Toeplitz matrices. The SDP is then solved
via the alternating direction method of multipliers (ADMM) where each iteration
involves only closed-form computations. In the NUPA case, the atomic-norm-based
formulation for channel estimation becomes nonconvex and a
gradient-decent-based algorithm is proposed to solve the problem. Simulation
results show that the proposed algorithms achieve better performance than the
CS-based and subspace-based algorithms
Subspace Tracking and Least Squares Approaches to Channel Estimation in Millimeter Wave Multiuser MIMO
The problem of MIMO channel estimation at millimeter wave frequencies, both
in a single-user and in a multi-user setting, is tackled in this paper. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation and the
orthogonal Oja algorithms to our framework and obtain two channel estimation
algorithms. We also present an alternative algorithm based on the least squares
approach. The hybrid analog/digital nature of the beamformer is also explicitly
taken into account at the algorithm design stage. In order to limit the system
complexity, a fixed analog beamformer is used at both sides of the
communication links. The obtained numerical results, showing the accuracy in
the estimation of the channel matrix dominant singular vectors, the system
achievable spectral efficiency, and the system bit-error-rate, prove that the
proposed algorithms are effective, and that they compare favorably, in terms of
the performance-complexity trade-off, with respect to several competing
alternatives.Comment: To appear on the IEEE Transactions on Communication
Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the
double directional (DD) channel model for downlink channel estimation. This
combination strikes a good balance between high-capacity communications and
parsimonious channel modeling, and also brings limited feedback schemes for
downlink channel state information within reach---since such channel can be
fully characterized by several key parameters. However, most existing channel
estimation work under the DD model has not yet considered DP arrays, perhaps
because of the complex array manifold and the resulting difficulty in algorithm
design. In this paper, we first reveal that the DD channel with DP arrays at
the transmitter and receiver can be naturally modeled as a low-rank tensor, and
thus the key parameters of the channel can be effectively estimated via tensor
decomposition algorithms.
On the theory side, we show that the DD-DP parameters are identifiable under
very mild conditions, by leveraging identifiability of low-rank tensors.
Furthermore, a compressed tensor decomposition algorithm is developed for
alleviating the downlink training overhead. We show that, by using judiciously
designed pilot structure, the channel parameters are still guaranteed to be
identified via the compressed tensor decomposition formulation even when the
size of the pilot sequence is much smaller than what is needed for conventional
channel identification methods, such as linear least squares and matched
filtering.
Numerical simulations are presented to showcase the effectiveness of the
proposed methods.Comment: matlab code is available at:
https://www.mathworks.com/matlabcentral/fileexchange/69176-tensor-based-channel-estimation-for-dual-polarized-mim
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