1,738 research outputs found
Spatial Intercell Interference Cancellation with CSI Training and Feedback
We investigate intercell interference cancellation (ICIC) with a practical
downlink training and uplink channel state information (CSI) feedback model.
The average downlink throughput for such a 2-cell network is derived. The user
location has a strong effect on the signal-to-interference ratio (SIR) and the
channel estimation error. This motivates adaptively switching between
traditional (single-cell) beamforming and ICIC at low signal-to-noise ratio
(SNR) where ICIC is preferred only with low SIR and accurate channel
estimation, and the use of ICIC with optimized training and feedback at high
SNR. For a given channel coherence time and fixed training and feedback
overheads, we develop optimal data vs. pilot power allocation for CSI training
as well as optimal feedback resource allocation to feed back CSI of different
channels. Both analog and finite-rate digital feedback are considered. With
analog feedback, the training power optimization provides a more significant
performance gain than feedback optimization; while conversely for digital
feedback, performance is more sensitive to the feedback bit allocation than the
training power optimization. We show that even with low-rate feedback and
standard training, ICIC can transform an interference-limited cellular network
into a noise-limited one.Comment: 24 pages, 10 figures, submitted to IEEE Trans. Wireless Commun., May,
201
Tranceiver Design using Linear Precoding in a Multiuser MIMO System with Limited Feedback
We investigate quantization and feedback of channel state information in a
multiuser (MU) multiple input multiple output (MIMO) system. Each user may
receive multiple data streams. Our design minimizes the sum mean squared error
(SMSE) while accounting for the imperfections in channel state information
(CSI) at the transmitter. This paper makes three contributions: first, we
provide an end-to-end SMSE transceiver design that incorporates receiver
combining, feedback policy and transmit precoder design with channel
uncertainty. This enables the proposed transceiver to outperform the previously
derived limited feedback MU linear transceivers. Second, we remove
dimensionality constraints on the MIMO system, for the scenario with multiple
data streams per user, using a combination of maximum expected signal combining
(MESC) and minimum MSE receiver. This makes the feedback of each user
independent of the others and the resulting feedback overhead scales linearly
with the number of data streams instead of the number of receiving antennas.
Finally, we analyze SMSE of the proposed algorithm at high signal-to-noise
ratio (SNR) and large number of transmit antennas. As an aside, we show
analytically why the bit error rate, in the high SNR regime, increases if
quantization error is ignored.Comment: Submitted to IET Journals in Communication
Joint Bi-Directional Training of Nonlinear Precoders and Receivers in Cellular Networks
Joint optimization of nonlinear precoders and receive filters is studied for
both the uplink and downlink in a cellular system. For the uplink, the base
transceiver station (BTS) receiver implements successive interference
cancellation, and for the downlink, the BTS station pre-compensates for the
interference with Tomlinson-Harashima precoding (THP). Convergence of
alternating optimization of receivers and transmitters in a single cell is
established when filters are updated according to a minimum mean squared error
(MMSE) criterion, subject to appropriate power constraints. Adaptive algorithms
are then introduced for updating the precoders and receivers in the absence of
channel state information, assuming time-division duplex transmissions with
channel reciprocity. Instead of estimating the channels, the filters are
directly estimated according to a least squares criterion via bi-directional
training: Uplink pilots are used to update the feedforward and feedback
filters, which are then used as interference pre-compensation filters for
downlink training of the mobile receivers. Numerical results show that
nonlinear filters can provide substantial gains relative to linear filters with
limited forward-backward iterations.Comment: 12 pages, 9 figures, submitted to IEEE Trans. Signal Process., Aug.
201
Multiuser MIMO Sequential Beamforming with Full-duplex Training
Multiple transmitting antennas can considerably increase the downlink
spectral efficiency by beamforming to multiple users at the same time. However,
multiuser beamforming requires channel state information (CSI) at the
transmitter, which leads to training overhead and reduces overall achievable
spectral efficiency. In this paper, we propose and analyze a sequential
beamforming strategy that utilizes full-duplex base station to implement
downlink data transmission concurrently with CSI acquisition via in-band closed
or open loop training. Our results demonstrate that full-duplex capability can
improve the spectral efficiency of uni-directional traffic, by leveraging it to
reduce the control overhead of CSI estimation. In moderate SNR regimes, we
analytically derive tight approximations for the optimal training duration and
characterize the associated respective spectral efficiency. We further
characterize the enhanced multiplexing gain performance in the high SNR regime.
In both regimes, the performance of the proposed full-duplex strategy is
compared to the half-duplex counterpart to quantify spectral efficiency
improvement. With experimental data [1] and 3D channel model [2] from 3GPP, in
a 1.4 MHz 8X8 system LTE system with the block length of 500 symbols, the
proposed strategy attains a spectral efficiency improvement of 130% and 8% with
closed and open loop training, respectively.Comment: Accepted on Sep-28-2016 for publications at IEEE Transactions on
Wireless Communications. Please check the IEEE website for the final
published versio
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
Achievable Rates of FDD Massive MIMO Systems with Spatial Channel Correlation
It is well known that the performance of frequency-division-duplex (FDD)
massive MIMO systems with i.i.d. channels is disappointing compared with that
of time-division-duplex (TDD) systems, due to the prohibitively large overhead
for acquiring channel state information at the transmitter (CSIT). In this
paper, we investigate the achievable rates of FDD massive MIMO systems with
spatially correlated channels, considering the CSIT acquisition dimensionality
loss, the imperfection of CSIT and the regularized-zero-forcing linear
precoder. The achievable rates are optimized by judiciously designing the
downlink channel training sequences and user CSIT feedback codebooks,
exploiting the multiuser spatial channel correlation. We compare our achievable
rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial
division and multiplexing (JSDM) scheme, by deriving the deterministic
equivalents of the achievable rates, based on popular channel models. It is
shown that, based on the proposed eigenspace channel estimation schemes, the
rate-gap between FDD systems and TDD systems is significantly narrowed, even
approached under moderate number of base station antennas. Compared to the JSDM
scheme, our proposal achieves dimensionality-reduction channel estimation
without channel pre-projection, and higher throughput for moderate number of
antennas and moderate to large channel coherence time, though at higher
computational complexity
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
Base Station Cooperation with Feedback Optimization: A Large System Analysis
In this paper, we study feedback optimization problems that maximize the
users' signal to interference plus noise ratio (SINR) in a two-cell MIMO
broadcast channel. Assuming the users learn their direct and interfering
channels perfectly, they can feed back this information to the base stations
(BSs) over the uplink channels. The BSs then use the channel information to
design their transmission scheme. Two types of feedback are considered: analog
and digital. In the analog feedback case, the users send their unquantized and
uncoded CSI over the uplink channels. In this context, given a user's fixed
transmit power, we investigate how he/she should optimally allocate it to feed
back the direct and interfering (or cross) CSI for two types of base station
cooperation schemes, namely, Multi-Cell Processing (MCP) and Coordinated
Beamforming (CBf). In the digital feedback case, the direct and cross link
channel vectors of each user are quantized separately, each using RVQ, with
different size codebooks. The users then send the index of the quantization
vector in the corresponding codebook to the BSs. Similar to the feedback
optimization problem in the analog feedback, we investigate the optimal bit
partitioning for the direct and interfering link for both types of cooperation.
We focus on regularized channel inversion precoding structures and perform our
analysis in the large system limit in which the number of users per cell ()
and the number of antennas per BS () tend to infinity with their ratio
held fixed
QoS Constrained Power Minimization in the MISO Broadcast Channel with Imperfect CSI
We consider the design of linear precoders and receivers in a Multiple-Input
Single-Output (MISO) Broadcast Channel (BC). We aim at minimizing the transmit
power while fullfiling a set of per-user Quality-of-Service (QoS) constraints
expressed in terms of per-user average rate requirements. The Channel State
Information (CSI) is assumed to be perfectly known at the receivers but only
partially at the transmitter. To solve the problem we transform the QoS
constraints into Minimum Mean Square Error (MMSE) constraints. We then leverage
the MSE duality between the BC and the Multiple Access Channel (MAC), as well
as standard interference functions in the dual MAC, to perform power
minimization by means of an Alternating Optimization (AO) algorithm. Problem
feasibility is also studied to determine whether the QoS constraints can be
fulfilled or not. Finally, we present an algorithm to balance the average rates
and manage situations that may be unfeasible or lead to an unacceptably high
transmit power
Robust Transmission for Massive MIMO Downlink with Imperfect CSI
In this paper, the design of robust linear precoders for the massive
multi-input multi-output (MIMO) downlink with imperfect channel state
information (CSI) is investigated. The imperfect CSI for each UE obtained at
the BS is modeled as statistical CSI under a jointly correlated channel model
with both channel mean and channel variance information, which includes the
effects of channel estimation error, channel aging and spatial correlation. The
design objective is to maximize the expected weighted sum-rate. By combining
the minorize-maximize (MM) algorithm with the deterministic equivalent method,
an algorithm for robust linear precoder design is derived. The proposed
algorithm achieves a stationary point of the expected weighted sum-rate
maximization problem. To reduce the computational complexity, two
low-complexity algorithms are then derived. One for the general case, and the
other for the case when all the channel means are zeros. For the later case, it
is proved that the beam domain transmission is optimal, and thus the precoder
design reduces to the power allocation optimization in the beam domain.
Simulation results show that the proposed robust linear precoder designs apply
to various mobile scenarios and achieve high spectral efficiency.Comment: 14 pages, 9 figures, Accepted by IEEE Transactions on Communication
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