861 research outputs found
Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations
This letter proposes a parametric sparse multiple input multiple output
(MIMO)-OFDM channel estimation scheme based on the finite rate of innovation
(FRI) theory, whereby super-resolution estimates of path delays with arbitrary
values can be achieved. Meanwhile, both the spatial and temporal correlations
of wireless MIMO channels are exploited to improve the accuracy of the channel
estimation. For outdoor communication scenarios, where wireless channels are
sparse in nature, path delays of different transmit-receive antenna pairs share
a common sparse pattern due to the spatial correlation of MIMO channels.
Meanwhile, the channel sparse pattern is nearly unchanged during several
adjacent OFDM symbols due to the temporal correlation of MIMO channels. By
simultaneously exploiting those MIMO channel characteristics, the proposed
scheme performs better than existing state-of-the-art schemes. Furthermore, by
joint processing of signals associated with different antennas, the pilot
overhead can be reduced under the framework of the FRI theory.Comment: This paper has been accepted by IEEE Communications Letter
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
Modulated Unit-Norm Tight Frames for Compressed Sensing
In this paper, we propose a compressed sensing (CS) framework that consists
of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a
column-wise orthonormal matrix. We prove that this structure satisfies the
restricted isometry property (RIP) with high probability if the number of
measurements for -sparse signals of length
and if the column-wise orthonormal matrix is bounded. Some existing structured
sensing models can be studied under this framework, which then gives tighter
bounds on the required number of measurements to satisfy the RIP. More
importantly, we propose several structured sensing models by appealing to this
unified framework, such as a general sensing model with arbitrary/determinisic
subsamplers, a fast and efficient block compressed sensing scheme, and
structured sensing matrices with deterministic phase modulations, all of which
can lead to improvements on practical applications. In particular, one of the
constructions is applied to simplify the transceiver design of CS-based channel
estimation for orthogonal frequency division multiplexing (OFDM) systems.Comment: submitted to IEEE Transactions on Signal Processin
Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels
Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid
precoding is challenging, since the number of radio frequency (RF) chains is
usually much smaller than that of antennas. To date, several channel estimation
schemes have been proposed for mmWave massive MIMO over narrow-band channels,
while practical mmWave channels exhibit the frequency-selective fading (FSF).
To this end, this letter proposes a multi-user uplink channel estimation scheme
for mmWave massive MIMO over FSF channels. Specifically, by exploiting the
angle-domain structured sparsity of mmWave FSF channels, a distributed
compressive sensing (DCS)-based channel estimation scheme is proposed.
Moreover, by using the grid matching pursuit strategy with adaptive measurement
matrix, the proposed algorithm can solve the power leakage problem caused by
the continuous angles of arrival or departure (AoA/AoD). Simulation results
verify that the good performance of the proposed solution.Comment: 4 pages, 3 figures, accepted by IEEE Communications Letters. This
paper may be the first one that investigates the frequency selective fading
channel estimation for mmWave massive MIMO systems with hybrid precoding. Key
words: Millimeter-wave (mmWave) massive MIMO, frequency-selective fading,
channel estimation, compressive sensing, hybrid precodin
Distributed Compressive Sensing Based Doubly Selective Channel Estimation for Large-Scale MIMO Systems
Doubly selective (DS) channel estimation in largescale multiple-input
multiple-output (MIMO) systems is a challenging problem due to the requirement
of unaffordable pilot overheads and prohibitive complexity. In this paper, we
propose a novel distributed compressive sensing (DCS) based channel estimation
scheme to solve this problem. In the scheme, we introduce the basis expansion
model (BEM) to reduce the required channel coefficients and pilot overheads.
And due to the common sparsity of all the transmit-receive antenna pairs in
delay domain, we estimate the BEM coefficients by considering the DCS
framework, which has a simple linear structure with low complexity. Further
more, a linear smoothing method is proposed to improve the estimation accuracy.
Finally, we conduct various simulations to verify the validity of the proposed
scheme and demonstrate the performance gains of the proposed scheme compared
with conventional schemes.Comment: conference,7 pages,5 figure
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
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is widely recognized as a
promising technology for future 5G wireless communication systems. To achieve
the theoretical performance gains in massive MIMO systems, accurate channel
state information at the transmitter (CSIT) is crucial. Due to the overwhelming
pilot signaling and channel feedback overhead, however, conventional downlink
channel estimation and uplink channel feedback schemes might not be suitable
for frequency-division duplexing (FDD) massive MIMO systems. In addition, these
two topics are usually separately considered in the literature. In this paper,
we propose a joint channel training and feedback scheme for FDD massive MIMO
systems. Specifically, we firstly exploit the temporal correlation of
time-varying channels to propose a differential channel training and feedback
scheme, which simultaneously reduces the overhead for downlink training and
uplink feedback. We next propose a structured compressive sampling matching
pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the
structured sparsity of wireless MIMO channels. Simulation results demonstrate
that the proposed scheme can achieve substantial reduction in the training and
feedback overhead
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
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
Efficient Beam Alignment for mmWave Single-Carrier Systems with Hybrid MIMO Transceivers
Communication at millimeter wave (mmWave) bands is expected to become a key
ingredient of next generation (5G) wireless networks. Effective mmWave
communications require fast and reliable methods for beamforming at both the
User Equipment (UE) and the Base Station (BS) sides, in order to achieve a
sufficiently large Signal-to-Noise Ratio (SNR) after beamforming. We refer to
the problem of finding a pair of strongly coupled narrow beams at the
transmitter and receiver as the Beam Alignment (BA) problem. In this paper, we
propose an efficient BA scheme for single-carrier mmWave communications. In the
proposed scheme, the BS periodically probes the channel in the downlink via a
pre-specified pseudo-random beamforming codebook and pseudo-random spreading
codes, letting each UE estimate the Angle-of-Arrival / Angle-of-Departure
(AoA-AoD) pair of the multipath channel for which the energy transfer is
maximum. We leverage the sparse nature of mmWave channels in the AoA-AoD domain
to formulate the BA problem as the estimation of a sparse non-negative vector.
Based on the recently developed Non-Negative Least Squares (NNLS) technique, we
efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We
evaluate the performance of the proposed scheme under a realistic channel
model, where the propagation channel consists of a few multipath scattering
components each having different delays, AoAs-AoDs, and Doppler shifts.The
channel model parameters are consistent with experimental channel measurements.
Simulation results indicate that the proposed method is highly robust to fast
channel variations caused by the large Doppler spread between the multipath
components. Furthermore, we also show that after achieving BA the beamformed
channel is essentially frequency-flat, such that single-carrier communication
needs no equalization in the time domain
- …