148 research outputs found
Principal Component Analysis (PCA)-based Massive-MIMO Channel Feedback
Channel-state-information (CSI) feedback methods are considered, especially
for massive or very large-scale multiple-input multiple-output (MIMO) systems.
To extract essential information from the CSI without redundancy that arises
from the highly correlated antennas, a receiver transforms (sparsifies) a
correlated CSI vector to an uncorrelated sparse CSI vector by using a
Karhunen-Loeve transform (KLT) matrix that consists of the eigen vectors of
covariance matrix of CSI vector and feeds back the essential components of the
sparse CSI, i.e., a principal component analysis method. A transmitter then
recovers the original CSI through the inverse transformation of the feedback
vector. Herein, to obtain the covariance matrix at transceiver, we derive
analytically the covariance matrix of spatially correlated Rayleigh fading
channels based on its statistics including transmit antennas' and receive
antennas' correlation matrices, channel variance, and channel delay profile.
With the knowledge of the channel statistics, the transceiver can readily
obtain the covariance matrix and KLT matrix. Compression feedback error and
bit-error-rate performance of the proposed method are analyzed. Numerical
results verify that the proposed method is promising, which reduces
significantly the feedback overhead of the massive-MIMO systems with marginal
performance degradation from full-CSI feedback (e.g., feedback amount reduction
by 80%, i.e., 1/5 of original CSI, with spectral efficiency reduction by only
2%). Furthermore, we show numerically that, for a given limited feedback
amount, we can find the optimal number of transmit antennas to achieve the
largest spectral efficiency, which is a new design framework.Comment: 10 pages, 5 figure
Multi-user Precoding and Channel Estimation for Hybrid Millimeter Wave Systems
In this paper, we develop a low-complexity channel estimation for hybrid
millimeter wave (mmWave) systems, where the number of radio frequency (RF)
chains is much less than the number of antennas equipped at each transceiver.
The proposed mmWave channel estimation algorithm first exploits multiple
frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base
station (BS) and user sides for the design of analog beamforming matrices. Then
all the users transmit orthogonal pilot symbols to the BS along the directions
of the estimated strongest AoAs in order to estimate the channel. The estimated
channel will be adopted to design the digital zero-forcing (ZF) precoder at the
BS for the multi-user downlink transmission. The proposed channel estimation
algorithm is applicable to both nonsparse and sparse mmWave channel
environments. Furthermore, we derive a tight achievable rate upper bound of the
digital ZF precoding with the proposed channel estimation algorithm scheme. Our
analytical and simulation results show that the proposed scheme obtains a
considerable achievable rate of fully digital systems, where the number of RF
chains equipped at each transceiver is equal to the number of antennas.
Besides, by taking into account the effect of various types of errors, i.e.,
random phase errors, transceiver analog beamforming errors, and equivalent
channel estimation errors, we derive a closed-form approximation for the
achievable rate of the considered scheme. We illustrate the robustness of the
proposed channel estimation and multi-user downlink precoding scheme against
the system imperfection.Comment: 15 pages, accepted for publication, JSAC 201
Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO
This paper shows that deep neural network (DNN) can be used for efficient and
distributed channel estimation, quantization, feedback, and downlink multiuser
precoding for a frequency-division duplex massive multiple-input
multiple-output system in which a base station (BS) serves multiple mobile
users, but with rate-limited feedback from the users to the BS. A key
observation is that the multiuser channel estimation and feedback problem can
be thought of as a distributed source coding problem. In contrast to the
traditional approach where the channel state information (CSI) is estimated and
quantized at each user independently, this paper shows that a joint design of
pilots and a new DNN architecture, which maps the received pilots directly into
feedback bits at the user side then maps the feedback bits from all the users
directly into the precoding matrix at the BS, can significantly improve the
overall performance. This paper further proposes robust design strategies with
respect to channel parameters and also a generalizable DNN architecture for
varying number of users and number of feedback bits. Numerical results show
that the DNN-based approach with short pilot sequences and very limited
feedback overhead can already approach the performance of conventional linear
precoding schemes with full CSI.Comment: 15 pages, 10 figures, This is the final version to be published in
IEEE Transactions on Wireless Communications, The source code for this paper
are available at: https://github.com/foadsohrabi/DL-DSC-FDD-Massive-MIM
Multiuser Millimeter Wave Beamforming Strategies with Quantized and Statistical CSIT
To alleviate the high cost of hardware in mmWave systems, hybrid
analog/digital precoding is typically employed. In the conventional two-stage
feedback scheme, the analog beamformer is determined by beam search and
feedback to maximize the desired signal power of each user. The digital
precoder is designed based on quantization and feedback of effective channel to
mitigate multiuser interference. Alternatively, we propose a one-stage feedback
scheme which effectively reduces the complexity of the signalling and feedback
procedure. Specifically, the second-order channel statistics are leveraged to
design digital precoder for interference mitigation while all feedback overhead
is reserved for precise analog beamforming. Under a fixed total feedback
constraint, we investigate the conditions under which the one-stage feedback
scheme outperforms the conventional two-stage counterpart. Moreover, a rate
splitting (RS) transmission strategy is introduced to further tackle the
multiuser interference and enhance the rate performance. Consider (1) RS
precoded by the one-stage feedback scheme and (2) conventional transmission
strategy precoded by the two-stage scheme with the same first-stage feedback as
(1) and also certain amount of extra second-stage feedback. We show that (1)
can achieve a sum rate comparable to that of (2). Hence, RS enables remarkable
saving in the second-stage training and feedback overhead.Comment: submitted to TW
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
Downlink Achievable Rate Analysis for FDD Massive MIMO Systems
Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods
CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning
In frequency-division duplexing systems, the downlink channel state
information (CSI) acquisition scheme leads to high training and feedback
overheads. In this paper, we propose an uplink-aided downlink channel
acquisition framework using deep learning to reduce these overheads. Unlike
most existing works that focus only on channel estimation or feedback modules,
to the best of our knowledge, this is the first study that considers the entire
downlink CSI acquisition process, including downlink pilot design, channel
estimation, and feedback. First, we propose an adaptive pilot design module by
exploiting the correlation in magnitude among bidirectional channels in the
angular domain to improve channel estimation. Next, to avoid the bit allocation
problem during the feedback module, we concatenate the complex channel and
embed the uplink channel magnitude to the channel reconstruction at the base
station. Lastly, we combine the above two modules and compare two popular
downlink channel acquisition frameworks. The former framework estimates and
feeds back the channel at the user equipment subsequently. The user equipment
in the latter one directly feeds back the received pilot signals to the base
station. Our results reveal that, with the help of uplink, directly feeding
back the pilot signals can save approximately 20% of feedback bits, which
provides a guideline for future research.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges
Precoding has been conventionally considered as an effective means of
mitigating the interference and efficiently exploiting the available in the
multiantenna downlink channel, where multiple users are simultaneously served
with independent information over the same channel resources. The early works
in this area were focused on transmitting an individual information stream to
each user by constructing weighted linear combinations of symbol blocks
(codewords). However, more recent works have moved beyond this traditional view
by: i) transmitting distinct data streams to groups of users and ii) applying
precoding on a symbol-per-symbol basis. In this context, the current survey
presents a unified view and classification of precoding techniques with respect
to two main axes: i) the switching rate of the precoding weights, leading to
the classes of block- and symbol-level precoding, ii) the number of users that
each stream is addressed to, hence unicast-/multicast-/broadcast- precoding.
Furthermore, the classified techniques are compared through representative
numerical results to demonstrate their relative performance and uncover
fundamental insights. Finally, a list of open theoretical problems and
practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial
DL-based CSI Feedback and Cooperative Recovery in Massive MIMO
In this paper, we exploit the correlation between nearby user equipment (UE)
and develop a deep learning-based channel state information (CSI) feedback and
cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The
CSI information can be divided into two parts: shared by nearby UE and owned by
individual UE. The key idea of exploiting the correlation is to reduce the
overhead used to repeatedly feedback shared information. Unlike in the general
autoencoder framework, an extra decoder and a combination network are added at
the base station to recover the shared information from the feedback CSI of two
nearby UE and combine the shared and individual information, respectively, but
no modification is performed at the UEs. For a UE with multiple antennas, we
also introduce a baseline neural network architecture with long short-term
memory modules to extract the correlation of nearby antennas. Given that the
CSI phase is not sparse, we propose two magnitude-dependent phase feedback
strategies that introduce statistical and instant CSI magnitude information to
the phase feedback process, respectively. Simulation results on two different
channel datasets show the effectiveness of the proposed CoCsiNet.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
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