327 research outputs found
Linear Precoding Designs for Amplify-and-Forward Multiuser Two-Way Relay Systems
Two-way relaying can improve spectral efficiency in two-user cooperative
communications. It also has great potential in multiuser systems. A major
problem of designing a multiuser two-way relay system (MU-TWRS) is transceiver
or precoding design to suppress co-channel interference. This paper aims to
study linear precoding designs for a cellular MU-TWRS where a multi-antenna
base station (BS) conducts bi-directional communications with multiple mobile
stations (MSs) via a multi-antenna relay station (RS) with amplify-and-forward
relay strategy. The design goal is to optimize uplink performance, including
total mean-square error (Total-MSE) and sum rate, while maintaining individual
signal-to-interference-plus-noise ratio (SINR) requirement for downlink
signals. We show that the BS precoding design with the RS precoder fixed can be
converted to a standard second order cone programming (SOCP) and the optimal
solution is obtained efficiently. The RS precoding design with the BS precoder
fixed, on the other hand, is non-convex and we present an iterative algorithm
to find a local optimal solution. Then, the joint BS-RS precoding is obtained
by solving the BS precoding and the RS precoding alternately. Comprehensive
simulation is conducted to demonstrate the effectiveness of the proposed
precoding designs.Comment: 13 pages, 12 figures, Accepted by IEEE TW
Coordinated Multicast Beamforming in Multicell Networks
We study physical layer multicasting in multicell networks where each base
station, equipped with multiple antennas, transmits a common message using a
single beamformer to multiple users in the same cell. We investigate two
coordinated beamforming designs: the quality-of-service (QoS) beamforming and
the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The
goal of the QoS beamforming is to minimize the total power consumption while
guaranteeing that received SINR at each user is above a predetermined
threshold. We present a necessary condition for the optimization problem to be
feasible. Then, based on the decomposition theory, we propose a novel
decentralized algorithm to implement the coordinated beamforming with limited
information sharing among different base stations. The algorithm is guaranteed
to converge and in most cases it converges to the optimal solution. The max-min
SINR (MMS) beamforming is to maximize the minimum received SINR among all users
under per-base station power constraints. We show that the MMS problem and a
weighted peak-power minimization (WPPM) problem are inverse problems. Based on
this inversion relationship, we then propose an efficient algorithm to solve
the MMS problem in an approximate manner. Simulation results demonstrate
significant advantages of the proposed multicast beamforming algorithms over
conventional multicasting schemes.Comment: 10pages, 9 figure
Massive MIMO Multicasting in Noncooperative Cellular Networks
We study the massive multiple-input multiple-output (MIMO) multicast
transmission in cellular networks where each base station (BS) is equipped with
a large-scale antenna array and transmits a common message using a single
beamformer to multiple mobile users. We first show that when each BS knows the
perfect channel state information (CSI) of its own served users, the
asymptotically optimal beamformer at each BS is a linear combination of the
channel vectors of its multicast users. Moreover, the optimal combining
coefficients are obtained in closed form. Then we consider the imperfect CSI
scenario where the CSI is obtained through uplink channel estimation in
timedivision duplex systems. We propose a new pilot scheme that estimates the
composite channel which is a linear combination of the individual channels of
multicast users in each cell. This scheme is able to completely eliminate pilot
contamination. The pilot power control for optimizing the multicast beamformer
at each BS is also derived. Numerical results show that the asymptotic
performance of the proposed scheme is close to the ideal case with perfect CSI.
Simulation also verifies the effectiveness of the proposed scheme with finite
number of antennas at each BS.Comment: to appear in IEEE JSAC Special Issue on 5G Wireless Communication
System
Secure Beamforming for MIMO Two-Way Communications with an Untrusted Relay
This paper studies the secure beamforming design in a multiple-antenna
three-node system where two source nodes exchange messages with the help of an
untrusted relay node. The relay acts as both an essential signal forwarder and
a potential eavesdropper. Both two-phase and three-phase two-way relay
strategies are considered. Our goal is to jointly optimize the source and relay
beamformers for maximizing the secrecy sum rate of the two-way communications.
We first derive the optimal relay beamformer structures. Then, iterative
algorithms are proposed to find source and relay beamformers jointly based on
alternating optimization. Furthermore, we conduct asymptotic analysis on the
maximum secrecy sum-rate. Our analysis shows that when all transmit powers
approach infinity, the two-phase two-way relay scheme achieves the maximum
secrecy sum rate if the source beamformers are designed such that the received
signals at the relay align in the same direction. This reveals an important
advantage of signal alignment technique in against eavesdropping. It is also
shown that if the source powers approach zero the three-phase scheme performs
the best while the two-phase scheme is even worse than direct transmission.
Simulation results have verified the efficiency of the secure beamforming
algorithms as well as the analytical findings.Comment: 10 figures, Submitted to IEEE Transactions on Signal Processin
Channel Estimation and Optimal Training Design for Correlated MIMO Two-Way Relay Systems in Colored Environment
In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcasting (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC and BC phases are then formulated and solved to improve channel estimation accuracy. For the general scenario of training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences that result in near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are derived. In order to reduce training overhead, the minimum required training length for channel estimation in both the MAC and BC phases are also derived. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
Graphical models have exhibited their performance in numerous tasks ranging
from biological analysis to recommender systems. However, graphical models with
hub nodes are computationally difficult to fit, particularly when the dimension
of the data is large. To efficiently estimate the hub graphical models, we
introduce a two-phase algorithm. The proposed algorithm first generates a good
initial point via a dual alternating direction method of multipliers (ADMM),
and then warm starts a semismooth Newton (SSN) based augmented Lagrangian
method (ALM) to compute a solution that is accurate enough for practical tasks.
The sparsity structure of the generalized Jacobian ensures that the algorithm
can obtain a nice solution very efficiently. Comprehensive experiments on both
synthetic data and real data show that it obviously outperforms the existing
state-of-the-art algorithms. In particular, in some high dimensional tasks, it
can save more than 70\% of the execution time, meanwhile still achieves a
high-quality estimation.Comment: 28 pages,3 figure
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