756 research outputs found
Closed-Loop Beam Alignment for Massive MIMO Channel Estimation
Training sequences are designed to probe wireless channels in order to obtain
channel state information for block-fading channels. Optimal training sounds
the channel using orthogonal beamforming vectors to find an estimate that
optimizes some cost function, such as mean square error. As the number of
transmit antennas increases, however, the training overhead becomes
significant. This creates a need for alternative channel estimation schemes for
increasingly large transmit arrays. In this work, we relax the orthogonal
restriction on sounding vectors. The use of a feedback channel after each
forward channel use during training enables closed-loop sounding vector design.
A misalignment cost function is introduced, which provides a metric to
sequentially design sounding vectors. In turn, the structure of the sounding
vectors aligns the transmit beamformer with the true channel direction, thereby
increasing beamforming gain. This beam alignment scheme for massive MIMO is
shown to improve beamforming gain over conventional orthogonal training for a
MISO channel
Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems
The use of large-scale antenna systems in future commercial wireless
communications is an emerging technology that uses an excess of transmit
antennas to realize high spectral efficiency. Achieving potential gains with
large-scale antenna arrays in practice hinges on sufficient channel estimation
accuracy. Much prior work focuses on TDD based networks, relying on reciprocity
between the uplink and downlink channels. However, most currently deployed
commercial wireless systems are FDD based, making it difficult to exploit
channel reciprocity. In massive MIMO FDD systems, the problem of channel
estimation becomes even more challenging due to the attendant substantial
training resources and feedback requirements which scale with the number of
antennas. In this paper, we consider the problem of training sequence design
that employs a set of training signals and its mapping to the training periods.
We focus on reduced-dimension training sequence designs, along with transmit
precoder designs, aimed at reducing both hardware complexity and power
consumption. The resulting designs are extended to hybrid analog-digital
beamforming systems, which employ a limited number of active RF chains for
transmit precoding, by applying the Toeplitz distribution theorem to
large-scale linear antenna systems. A practical guideline for training sequence
parameter selection is presented along with performance analysis.Comment: 16 pages, 9 figures, replaced with revised versio
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 Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is
a newly introduced architecture that enables both spatial multiplexing and
beamforming while facilitating highly reconfigurable hardware implementation in
millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on
channel state information (CSI) acquisition and hybrid precoding. As benefited
from a coordinated and open-loop pilot beam pattern design, all the sub-arrays
can perform channel sounding with less training overhead compared with the
traditional orthogonal operation of each sub-array. Furthermore, two sparse
channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP)
and joint sparse Bayesian learning with reweighting (JSBL-),
are proposed to exploit the hidden structured sparsity in the beam-domain
channel vector. Finally, successive interference cancellation (SIC) based
hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO
system, which decomposes the joint sub-array RF beamformer design into an
interactive per-sub-array-group handle. Simulation results show that the
proposed two channel estimators fully take advantage of the partial coupling
characteristic of DPA-MIMO channels to perform channel recovery, and the
proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays
architecture with satisfactory performance and low complexity.Comment: accepted by IEEE Transactions on Vehicular Technolog
Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels
In this paper, we propose trellis coded quantization (TCQ) based limited
feedback techniques for massive multiple-input single-output (MISO) frequency
division duplexing (FDD) systems in temporally and spatially correlated
channels. We exploit the correlation present in the channel to effectively
quantize channel direction information (CDI). For multiuser (MU) systems with
matched-filter (MF) precoding, we show that the number of feedback bits
required by the random vector quantization (RVQ) codebook to match even a small
fraction of the perfect CDI signal-to-interference-plus-noise ratio (SINR)
performance is large. With such large numbers of bits, the exhaustive search
required by conventional codebook approaches make them infeasible for massive
MISO systems. Motivated by this, we propose a differential TCQ scheme for
temporally correlated channels that transforms the source constellation at each
stage in a trellis using 2D translation and scaling techniques. We derive a
scaling parameter for the source constellation as a function of the temporal
correlation and the number of BS antennas. We also propose a TCQ based limited
feedback scheme for spatially correlated channels where the channel is
quantized directly without performing decorrelation at the receiver. Simulation
results show that the proposed TCQ schemes outperform the existing noncoherent
TCQ (NTCQ) schemes, by improving the spectral efficiency and beamforming gain
of the system. The proposed differential TCQ also reduces the feedback overhead
of the system compared to the differential NTCQ method.Comment: 13 pages, 18 figures, IEEE Transactions on Vehicular Technology,
accepted for publicatio
Two-Stage Beamformer Design for Massive MIMO Downlink By Trace Quotient Formulation
In this paper, the problem of outer beamformer design based only on channel
statistic information is considered for two-stage beamforming for multi-user
massive MIMO downlink, and the problem is approached based on
signal-to-leakage-plus-noise ratio (SLNR). To eliminate the dependence on the
instantaneous channel state information, a lower bound on the average SLNR is
derived by assuming zero-forcing (ZF) inner beamforming, and an outer
beamformer design method that maximizes the lower bound on the average SLNR is
proposed. It is shown that the proposed SLNR-based outer beamformer design
problem reduces to a trace quotient problem (TQP), which is often encountered
in the field of machine learning. An iterative algorithm is presented to obtain
an optimal solution to the proposed TQP. The proposed method has the capability
of optimally controlling the weighting factor between the signal power to the
desired user and the interference leakage power to undesired users according to
different channel statistics. Numerical results show that the proposed outer
beamformer design method yields significant performance gain over existing
methods.Comment: 27 pages, 5 figures, submitted to IEEE Transactions on Communication
Pilot Signal Design for Massive MIMO Systems: A Received Signal-To-Noise-Ratio-Based Approach
In this paper, the pilot signal design for massive MIMO systems to maximize
the training-based received signal-to-noise ratio (SNR) is considered under two
channel models: block Gauss-Markov and block independent and identically
distributed (i.i.d.) channel models. First, it is shown that under the block
Gauss-Markov channel model, the optimal pilot design problem reduces to a
semi-definite programming (SDP) problem, which can be solved numerically by a
standard convex optimization tool. Second, under the block i.i.d. channel
model, an optimal solution is obtained in closed form. Numerical results show
that the proposed method yields noticeably better performance than other
existing pilot design methods in terms of received SNR.Comment: 5 pages, double column, 1 figure. Submitted to IEEE Signal Processing
Letter
Trellis-Extended Codebooks and Successive Phase Adjustment: A Path from LTE-Advanced to FDD Massive MIMO Systems
It is of great interest to develop efficient ways to acquire accurate channel
state information (CSI) for frequency division duplexing (FDD) massive
multiple-input multiple-output (MIMO) systems for backward compatibility. It is
theoretically well known that the codebook size for CSI quantization should be
increased as the number of transmit antennas becomes larger, and 3GPP long term
evolution (LTE) and LTE-Advanced codebooks also follow this trend. Thus, in
massive MIMO, it is hard to apply the conventional approach of using
pre-defined vector-quantized codebooks for CSI quantization mainly because of
codeword search complexity. In this paper, we propose a trellis-extended
codebook (TEC) that can be easily harmonized with current wireless standards
such as LTE or LTE-Advanced by extending standardized codebooks designed for 2,
4, or 8 antennas with trellis structures. TEC exploits a Viterbi decoder and
convolutional encoder in channel coding as the CSI quantizer and the CSI
reconstructer, respectively. By quantizing multiple channel entries
simultaneously using standardized codebooks in a state transition of trellis
search, TEC can achieve fractional bits per channel entry quantization to have
a practical feedback overhead. Thus, TEC can solve both the complexity and the
feedback overhead issues of CSI quantization in massive MIMO systems. We also
develop trellis-extended successive phase adjustment (TE-SPA) which works as a
differential codebook of TEC. This is similar to the dual codebook concept of
LTE-Advanced. TE-SPA can reduce CSI quantization error even with lower feedback
overhead in temporally correlated channels. Numerical results verify the
effectiveness of the proposed schemes in FDD massive MIMO systems.Comment: 10 pages, 11 figures, accepted to IEEE Transactions on Wireless
Communications, Nov. 201
Leveraging the Restricted Isometry Property: Improved Low-Rank Subspace Decomposition for Hybrid Millimeter-Wave Systems
Communication at millimeter wave frequencies will be one of the essential new
technologies in 5G. Acquiring an accurate channel estimate is the key to
facilitate advanced millimeter wave hybrid multiple-input multiple-output
(MIMO) precoding techniques. Millimeter wave MIMO channel estimation, however,
suffers from a considerably increased channel use overhead. This happens due to
the limited number of radio frequency (RF) chains that prevent the digital
baseband from directly accessing the signal at each antenna. To address this
issue, recent research has focused on adaptive closed-loop and two-way channel
estimation techniques. In this paper, unlike the prior approaches, we study a
non-adaptive, hence rather simple, open-loop millimeter wave MIMO channel
estimation technique. We present a simple random design of channel subspace
sampling signals and show that they obey the restricted isometry property (RIP)
with high probability. We then formulate the channel estimation as a low-rank
subspace decomposition problem and, based on the RIP, show that the proposed
framework reveals resilience to a low signal-to-noise ratio. It is revealed
that the required number of channel uses ensuring a bounded estimation error is
linearly proportional to the degrees of freedom of the channel, whereas it
converges to a constant value if the number of RF chains can grow
proportionally to the channel dimension while keeping the channel rank fixed.
In particular, we show that the tighter the RIP characterization the lower the
channel estimation error is. We also devise an iterative technique that
effectively finds a suboptimal but stationary solution to the formulated
problem. The proposed technique is shown to have improved channel estimation
accuracy with a low channel use overhead as compared to that of previous
closed-loop and two-way adaptation techniques
A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems
In millimeter wave (mmWave) communication systems, existing hybrid
beamforming solutions generally require a large number of high-resolution phase
shifters (PSs) to realize analog beamformers, which still suffer from high
hardware complexity and power consumption. Targeting at this problem, this
article introduces a novel hardware-efficient hybrid precoding/combining
architecture, which only employs a limited number of simple phase over-samplers
(POSs) and a switch (SW) network to achieve maximum hardware efficiency while
maintaining satisfactory spectral efficiency performance. The POS can be
realized by a simple circuit and simultaneously outputs several parallel
signals with different phases. With the aid of a simple switch network, the
analog precoder/combiner is implemented by feeding the signals with appropriate
phases to antenna arrays or RF chains. We analyze the design challenges of this
POS-SW-based hybrid beamforming architecture and present potential solutions to
the fundamental issues, especially the precoder/combiner design and the channel
estimation strategy. Simulation results demonstrate that this
hardware-efficient structure can achieve comparable spectral efficiency but
much higher energy efficiency than that of the traditional structures
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