377 research outputs found
Interference Alignment with Analog Channel State Feedback
Interference alignment (IA) is a multiplexing gain optimal transmission
strategy for the interference channel. While the achieved sum rate with IA is
much higher than previously thought possible, the improvement often comes at
the cost of requiring network channel state information at the transmitters.
This can be achieved by explicit feedback, a flexible yet potentially costly
approach that incurs large overhead. In this paper we propose analog feedback
as an alternative to limited feedback or reciprocity based alignment. We show
that the full multiplexing gain observed with perfect channel knowledge is
preserved by analog feedback and that the mean loss in sum rate is bounded by a
constant when signal-to-noise ratio is comparable in both forward and feedback
channels. When signal-to-noise ratios are not quite symmetric, a fraction of
the multiplexing gain is achieved. We consider the overhead of training and
feedback and use this framework to optimize the system's effective throughput.
We present simulation results to demonstrate the performance of IA with analog
feedback, verify our theoretical analysis, and extend our conclusions on
optimal training and feedback length.Comment: accepted, to appear in IEEE Transactions on Wireless Communication
Broadcast Channels with Delayed Finite-Rate Feedback: Predict or Observe?
Most multiuser precoding techniques require accurate transmitter channel
state information (CSIT) to maintain orthogonality between the users. Such
techniques have proven quite fragile in time-varying channels because the CSIT
is inherently imperfect due to estimation and feedback delay, as well
quantization noise. An alternative approach recently proposed by Maddah-Ali and
Tse (MAT) allows for significant multiplexing gain in the multi-input
single-output (MISO) broadcast channel (BC) even with transmit CSIT that is
completely stale, i.e. uncorrelated with the current channel state. With
users, their scheme claims to lose only a factor relative to the full
degrees of freedom (DoF) attainable in the MISO BC with perfect CSIT for
large . However, their result does not consider the cost of the feedback,
which is potentially very large in high mobility (short channel coherence
time). In this paper, we more closely examine the MAT scheme and compare its
DoF gain to single user transmission (which always achieves 1 DoF) and partial
CSIT linear precoding (which achieves up to ). In particular, assuming the
channel coherence time is symbol periods and the feedback delay is we show that when (short coherence time), single
user transmission performs best, whereas for (long coherence time), zero-forcing precoding
outperforms the other two. The MAT scheme is optimal for intermediate coherence
times, which for practical parameter choices is indeed quite a large and
significant range, even accounting for the feedback cost.Comment: 25 pages, 4 figures, submitted to IEEE Transactions on Wireless
Communications, May 201
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
On the Degrees of Freedom of time correlated MISO broadcast channel with delayed CSIT
We consider the time correlated MISO broadcast channel where the transmitter
has partial knowledge on the current channel state, in addition to delayed
channel state information (CSI). Rather than exploiting only the current CSI,
as the zero-forcing precoding, or only the delayed CSI, as the Maddah-Ali-Tse
(MAT) scheme, we propose a seamless strategy that takes advantage of both. The
achievable degrees of freedom of the proposed scheme is characterized in terms
of the quality of the current channel knowledge.Comment: 7 pages, 1 figure, submitted to ISIT 2012, extended version with
detailed proof
Eigen-Inference Precoding for Coarsely Quantized Massive MU-MIMO System with Imperfect CSI
This work considers the precoding problem in massive multiuser multiple-input
multiple-output (MU-MIMO) systems equipped with low-resolution
digital-to-analog converters (DACs). In previous literature on this topic, it
is commonly assumed that the channel state information (CSI) is perfectly
known. However, in practical applications the CSI is inevitably contaminated by
noise. In this paper, we propose, for the first time, an eigen-inference (EI)
precoding scheme to improve the error performance of the coarsely quantized
massive MU-MIMO systems under imperfect CSI, which is mathematically modeled by
a sum of two rectangular random matrices (RRMs). Instead of performing analysis
based on the RRM, using Girko's Hermitization trick, the proposed method
leverages the block random matrix theory by augmenting the RRM into a block
symmetric channel matrix (BSCA). Specially, we derive the empirical
distribution of the eigenvalues of the BSCA and establish the limiting spectra
distribution connection between the true BSCA and its noisy observation. Then,
based on these theoretical results, we propose an EI-based moments matching
method for CSI-related noise level estimation and a rotation invariant
estimation method for CSI reconstruction. Based on the cleaned CSI, the
quantized precoding problem is tackled via the Bussgang theorem and the
Lagrangian multiplier method. The prosed methods are lastly verified by
numerical simulations and the results demonstrate the effectiveness of the
proposed precoder
Location-Aided Coordinated Analog Precoding for Uplink Multi-User Millimeter Wave Systems
Millimeter wave (mmWave) communication is expected to play an important role
in next generation cellular networks, aiming to cope with the bandwidth
shortage affecting conventional wireless carriers. Using side-information has
been proposed as a potential approach to accelerate beam selection in mmWave
massive MIMO (m-MIMO) communications. However, in practice, such information is
not error-free, leading to performance degradation. In the multi-user case, a
wrong beam choice might result in irreducible inter-user interference at the
base station (BS) side. In this paper, we consider location-aided precoder
design in a mmWave uplink scenario with multiple users (UEs). Assuming the
existence of direct device-to-device (D2D) links, we propose a decentralized
coordination mechanism for robust fast beam selection. The algorithm allows for
improved treatment of interference at the BS side and in turn leads to greater
spectral efficiencies.Comment: 17 pages, 4 figure
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
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
Active Learning and CSI Acquisition for mmWave Initial Alignment
Millimeter wave (mmWave) communication with large antenna arrays is a
promising technique to enable extremely high data rates due to the large
available bandwidth in mmWave frequency bands. In addition, given the knowledge
of an optimal directional beamforming vector, large antenna arrays have been
shown to overcome both the severe signal attenuation in mmWave as well as the
interference problem. However, fundamental limits on achievable learning rate
of an optimal beamforming vector remain.
This paper considers the problem of adaptive and sequential optimization of
the beamforming vectors during the initial access phase of communication. With
a single-path channel model, the problem is reduced to actively learning the
Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station
(BS). Drawing on the recent results in the design of a hierarchical beamforming
codebook [1], sequential measurement dependent noisy search strategies [2], and
active learning from an imperfect labeler [3], an adaptive and sequential
alignment algorithm is proposed.
An upper bound on the expected search time of the proposed algorithm is
derived via Extrinsic Jensen-Shannon Divergence. which demonstrates that the
search time of the proposed algorithm asymptotically matches the performance of
the noiseless bisection search up to a constant factor. Furthermore, the upper
bound shows that the acquired AoA error probability decays exponentially fast
with the search time with an exponent that is a decreasing function of the
acquisition rate.
Numerically, the proposed algorithm is compared with prior work where a
significant improvement of the system communication rate is observed. Most
notably, in the relevant regime of low (-10dB to 5dB) raw SNR, this establishes
the first practically viable solution for initial access and, hence, the first
demonstration of stand-alone mmWave communicationComment: This paper appears in: IEEE Journal on Selected Areas in
Communications On page(s): 1-16 Print ISSN: 0733-8716 Online ISSN: 1558-000
Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems
Hybrid beamforming is known to be a cost-effective and wide-spread solution
for a system with large-scale antenna arrays. This work studies the
optimization of the analog and digital components of the hybrid beamforming
solution for remote radio heads (RRHs) in a downlink cloud radio access network
(C-RAN) architecture. Digital processing is carried out at a baseband
processing unit (BBU) in the "cloud" and the precoded baseband signals are
quantized prior to transmission to the RRHs via finite-capacity fronthaul
links. In this system, we consider two different channel state information
(CSI) scenarios: 1) ideal CSI at the BBU 2) imperfect effective CSI.
Optimization of digital beamforming and fronthaul quantization strategies at
the BBU as well as analog radio frequency (RF) beamforming at the RRHs is a
coupled problem, since the effect of the quantization noise at the receiver
depends on the precoding matrices. The resulting joint optimization problem is
examined with the goal of maximizing the weighted downlink sum-rate and the
network energy efficiency. Fronthaul capacity and per-RRH power constraints are
enforced along with constant modulus constraint on the RF beamforming matrices.
For the case of perfect CSI, a block coordinate descent scheme is proposed
based on the weighted minimum-mean-square-error approach by relaxing the
constant modulus constraint of the analog beamformer. Also, we present the
impact of imperfect CSI on the weighted sum-rate and network energy efficiency
performance, and the algorithm is extended by applying the sample average
approximation. Numerical results confirm the effectiveness of the proposed
scheme and show that the proposed algorithm is robust to estimation errors
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