259 research outputs found
Interference-Alignment and Soft-Space-Reuse Based Cooperative Transmission for Multi-cell Massive MIMO Networks
As a revolutionary wireless transmission strategy, interference alignment
(IA) can improve the capacity of the cell-edge users. However, the acquisition
of the global channel state information (CSI) for IA leads to unacceptable
overhead in the massive MIMO systems. To tackle this problem, in this paper, we
propose an IA and soft-space-reuse (IA-SSR) based cooperative transmission
scheme under the two-stage precoding framework. Specifically, the cell-center
and the cell-edge users are separately treated to fully exploit the spatial
degrees of freedoms (DoF). Then, the optimal power allocation policy is
developed to maximize the sum-capacity of the network. Next, a low-cost channel
estimator is designed for the proposed IA-SSR framework. Some practical issues
in IA-SSR implementation are also discussed. Finally, plenty of numerical
results are presented to show the efficiency of the proposed algorithm.Comment: Submitted to IEEE Transactions on Wireless Communication
Harvest the potential of massive MIMO with multi-layer techniques
Massive MIMO is envisioned as a promising technology for 5G wireless networks
due to its high potential to improve both spectral and energy efficiency.
Although the massive MIMO system is based on innovations in the physical layer,
the upper layer techniques also play important roles in harvesting the
performance gains of massive MIMO. In this article, we begin with an analysis
of the benefits and challenges of massive MIMO systems. We then investigate the
multi-layer techniques for incorporating massive MIMO in several important
network deployment scenarios. We conclude this article with a discussion of
open and potential problems for future research.Comment: IEEE Networ
Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach
In this paper, we propose a novel channel feedback scheme for frequency
division duplexing massive multi-input multi-output systems. The concept uses
the notion of user statistical separability which was hinted in several prior
works in the massive antenna regime but not fully exploited so far. We here
propose a hybrid statistical-instantaneous feedback scheme based on a user
classification mechanism where the classification metric derives from a rate
bound analysis. According to classification results, a user either operates on
a statistical feedback mode or instantaneous mode. Our results illustrate the
sum rate advantages of our scheme under a global feedback overhead constraint.Comment: 5 pages, 4 figures, conference paper, 2018 Asilomar Conference on
Signals, Systems, and Computer
A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems
In this paper, a novel covariance-based channel feedback mechanism is
investigated for frequency division duplexing (FDD) massive multi-input
multi-output (MIMO) systems. The concept capitalizes on the notion of user
statistical separability which was hinted in several prior works in the massive
antenna regime but not fully exploited so far. We here propose a hybrid
statistical-instantaneous feedback mechanism where the users are separated into
two classes of feedback design based on their channel covariance. Under the
hybrid framework, each user either operates on a statistical feedback mode or
quantized instantaneous channel feedback mode depending on their so-called
statistical isolability. The key challenge lies in the design of a
covariance-aware classification algorithm which can handle the complex mutual
interactions between all users. The classification is derived from rate bound
principles. A suitable precoding method is also devised under the mixed
statistical and instantaneous feedback model. Simulations are performed to
validate our analytical results and illustrate the sum rate advantages of the
proposed feedback scheme under a global feedback overhead constraint.Comment: 31 pages, 9 figure
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Channel Estimation in Massive MIMO Systems
We introduce novel blind and semi-blind channel estimation methods for
cellular time-division duplexing systems with a large number of antennas at
each base station. The methods are based on the maximum a-posteriori principle
given a prior for the distribution of the channel vectors and the received
signals from the uplink training and data phases. Contrary to the
state-of-the-art massive MIMO channel estimators which either perform linear
estimation based on the pilot symbols or rely on a blind principle, the
proposed semi-blind method efficiently suppresses most of the interference
caused by pilot-contamination. The simulative analysis illustrates that the
semi-blind estimator outperforms state- of-the-art linear and non-linear
approaches to the massive MIMO channel estimation problem
Codebook Design for Channel Feedback in Lens-Based Millimeter-Wave Massive MIMO Systems
The number of radio frequency (RF) chains can be reduced through beam
selection in lens-based millimeter-wave (mmWave) massive MIMO systems, where
the equivalent channel between RF chains and multiple users is required at the
BS to achieve the multi-user multiplexing gain. However, to the best of our
knowledge, there is no dedicated codebook for the equivalent channel feedback
in such systems. In this paper, we propose the dimension-reduced subspace
codebook, which achieves a significant reduction of the feedback overhead and
codebook size. Specifically, we firstly utilize the limited scattering property
of mmWave channels to generate the high-dimensional vectors in the channel
subspace. Then, according to the function of lens and beam selector, we propose
the dimension-reduced subspace codebook to quantize the equivalent channel
vector.Moreover, the performance analysis of the proposed codebook is also
provided.Finally, simulation results show the superior performance of the
proposed dimension-reduced subspace codebook compared with conventional
codebooks.Comment: Submitted to SPL for publicatio
Joint Spatial Division and Multiplexing
We propose Joint Spatial Division and Multiplexing (JSDM), an approach to
multiuser MIMO downlink that exploits the structure of the correlation of the
channel vectors in order to allow for a large number of antennas at the base
station while requiring reduced-dimensional Channel State Information at the
Transmitter (CSIT). This allows for significant savings both in the downlink
training and in the CSIT feedback from the user terminals to the base station,
thus making the use of a large number of base station antennas potentially
suitable also for Frequency Division Duplexing (FDD) systems, for which
uplink/downlink channel reciprocity cannot be exploited. JSDM forms the
multiuser MIMO downlink precoder by concatenating a pre-beamforming matrix,
which depends only on the channel second-order statistics, with a classical
multiuser precoder, based on the instantaneous knowledge of the resulting
reduced dimensional effective channels. We prove a simple condition under which
JSDM incurs no loss of optimality with respect to the full CSIT case. For
linear uniformly spaced arrays, we show that such condition is closely
approached when the number of antennas is large. For this case, we use Szego
asymptotic theory of large Toeplitz matrices to design a DFT-based
pre-beamforming scheme requiring only coarse information about the users angles
of arrival and angular spread. Finally, we extend these ideas to the case of a
two-dimensional base station antenna array, with 3-dimensional beamforming,
including multiple beams in the elevation angle direction. We provide
guidelines for the pre-beamforming optimization and calculate the system
spectral efficiency under proportional fairness and maxmin fairness criteria,
showing extremely attractive performance. Our numerical results are obtained
via an asymptotic random matrix theory tool known as deterministic equivalent
approximation.Comment: 10 figure
Multi-User Flexible Coordinated Beamforming using Lattice Reduction for Massive MIMO Systems
The application of precoding algorithms in multi-user massive multiple-input
multiple-output (MU-Massive-MIMO) systems is restricted by the dimensionality
constraint that the number of transmit antennas has to be greater than or equal
to the total number of receive antennas. In this paper, a lattice reduction
(LR)-aided flexible coordinated beamforming (LR-FlexCoBF) algorithm is proposed
to overcome the dimensionality constraint in overloaded MU-Massive-MIMO
systems. A random user selection scheme is integrated with the proposed
LR-FlexCoBF to extend its application to MU-Massive-MIMO systems with arbitary
overloading levels. Simulation results show that significant improvements in
terms of bit error rate (BER) and sum-rate performances can be achieved by the
proposed LR-FlexCoBF precoding algorithm.Comment: 5 figures, Eusipc
Efficient Downlink Channel Probing and Uplink Feedback in FDD Massive MIMO Systems
Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of
multi-user MIMO in which the number of antennas at each Base Station (BS) is
very large and typically much larger than the number of users simultaneously
served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or
Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are
particularly desirable due to their implementation in current wireless networks
and their efficiency in situations with symmetric traffic and delay-sensitive
applications. However, implementing FDD massive MIMO systems is known to be
challenging since it imposes a large feedback overhead in the Uplink (UL) to
obtain channel state information for the Downlink (DL). In recent years, a
considerable amount of research is dedicated to developing methods to reduce
the feedback overhead in such systems. In this paper, we use the sparse spatial
scattering properties of the environment to achieve this goal. The idea is to
estimate the support of the continuous, frequency-invariant scattering function
from UL channel observations and use this estimate to obtain the support of the
DL channel vector via appropriate interpolation. We use the resulting support
estimate to design an efficient DL probing and UL feedback scheme in which the
feedback dimension scales proportionally with the sparsity order of DL channel
vectors. Since the sparsity order is much less than the number of BS antennas
in almost all practically relevant scenarios, our method incurs much less
feedback overhead compared with the currently proposed methods in the
literature, such as those based on compressed-sensing. We use numerical
simulations to assess the performance of our probing-feedback algorithm and
compare it with these methods.Comment: 24 pages, 10 figure
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