1,123 research outputs found
Multicell Coordinated Beamforming with Rate Outage Constraint--Part I: Complexity Analysis
This paper studies the coordinated beamforming (CoBF) design in the
multiple-input single-output interference channel, assuming only channel
distribution information given a priori at the transmitters. The CoBF design is
formulated as an optimization problem that maximizes a predefined system
utility, e.g., the weighted sum rate or the weighted max-min-fairness (MMF)
rate, subject to constraints on the individual probability of rate outage and
power budget. While the problem is non-convex and appears difficult to handle
due to the intricate outage probability constraints, so far it is still unknown
if this outage constrained problem is computationally tractable. To answer
this, we conduct computational complexity analysis of the outage constrained
CoBF problem. Specifically, we show that the outage constrained CoBF problem
with the weighted sum rate utility is intrinsically difficult, i.e., NP-hard.
Moreover, the outage constrained CoBF problem with the weighted MMF rate
utility is also NP-hard except the case when all the transmitters are equipped
with single antenna. The presented analysis results confirm that efficient
approximation methods are indispensable to the outage constrained CoBF problem.Comment: submitted to IEEE Transactions on Signal Processin
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
Aiming at solving large-scale learning problems, this paper studies
distributed optimization methods based on the alternating direction method of
multipliers (ADMM). By formulating the learning problem as a consensus problem,
the ADMM can be used to solve the consensus problem in a fully parallel fashion
over a computer network with a star topology. However, traditional synchronized
computation does not scale well with the problem size, as the speed of the
algorithm is limited by the slowest workers. This is particularly true in a
heterogeneous network where the computing nodes experience different
computation and communication delays. In this paper, we propose an asynchronous
distributed ADMM (AD-AMM) which can effectively improve the time efficiency of
distributed optimization. Our main interest lies in analyzing the convergence
conditions of the AD-ADMM, under the popular partially asynchronous model,
which is defined based on a maximum tolerable delay of the network.
Specifically, by considering general and possibly non-convex cost functions, we
show that the AD-ADMM is guaranteed to converge to the set of
Karush-Kuhn-Tucker (KKT) points as long as the algorithm parameters are chosen
appropriately according to the network delay. We further illustrate that the
asynchrony of the ADMM has to be handled with care, as slightly modifying the
implementation of the AD-ADMM can jeopardize the algorithm convergence, even
under a standard convex setting.Comment: 37 page
Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems
This work examines the use of two-way training to efficiently discriminate
the channel estimation performances at a legitimate receiver (LR) and an
unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless
system. This work improves upon the original discriminatory channel estimation
(DCE) scheme proposed by Chang et al where multiple stages of feedback and
retraining were used. While most studies on physical layer secrecy are under
the information-theoretic framework and focus directly on the data transmission
phase, studies on DCE focus on the training phase and aim to provide a
practical signal processing technique to discriminate between the channel
estimation performances at LR and UR. A key feature of DCE designs is the
insertion of artificial noise (AN) in the training signal to degrade the
channel estimation performance at UR. To do so, AN must be placed in a
carefully chosen subspace based on the transmitter's knowledge of LR's channel
in order to minimize its effect on LR. In this paper, we adopt the idea of
two-way training that allows both the transmitter and LR to send training
signals to facilitate channel estimation at both ends. Both reciprocal and
non-reciprocal channels are considered and a two-way DCE scheme is proposed for
each scenario. {For mathematical tractability, we assume that all terminals
employ the linear minimum mean square error criterion for channel estimation.
Based on the mean square error (MSE) of the channel estimates at all
terminals,} we formulate and solve an optimization problem where the optimal
power allocation between the training signal and AN is found by minimizing the
MSE of LR's channel estimate subject to a constraint on the MSE achievable at
UR. Numerical results show that the proposed DCE schemes can effectively
discriminate between the channel estimation and hence the data detection
performances at LR and UR.Comment: 1
Coordinated Multicasting with Opportunistic User Selection in Multicell Wireless Systems
Physical layer multicasting with opportunistic user selection (OUS) is
examined for multicell multi-antenna wireless systems. By adopting a two-layer
encoding scheme, a rate-adaptive channel code is applied in each fading block
to enable successful decoding by a chosen subset of users (which varies over
different blocks) and an application layer erasure code is employed across
multiple blocks to ensure that every user is able to recover the message after
decoding successfully in a sufficient number of blocks. The transmit signal and
code-rate in each block determine opportunistically the subset of users that
are able to successfully decode and can be chosen to maximize the long-term
multicast efficiency. The employment of OUS not only helps avoid
rate-limitations caused by the user with the worst channel, but also helps
coordinate interference among different cells and multicast groups. In this
work, efficient algorithms are proposed for the design of the transmit
covariance matrices, the physical layer code-rates, and the target user subsets
in each block. In the single group scenario, the system parameters are
determined by maximizing the group-rate, defined as the physical layer
code-rate times the fraction of users that can successfully decode in each
block. In the multi-group scenario, the system parameters are determined by
considering a group-rate balancing optimization problem, which is solved by a
successive convex approximation (SCA) approach. To further reduce the feedback
overhead, we also consider the case where only part of the users feed back
their channel vectors in each block and propose a design based on the balancing
of the expected group-rates. In addition to SCA, a sample average approximation
technique is also introduced to handle the probabilistic terms arising in this
problem. The effectiveness of the proposed schemes is demonstrated by computer
simulations.Comment: Accepted by IEEE Transactions on Signal Processin
Synthesis of CuInSe2 thin films from electrodeposited Cu11In9 precursors by two-step annealing
In this study, copper indium selenide (CIS) films were synthesized from electrodeposited Cu-In-Se precursors by two-step annealing. The agglomeration phenomenon of the electrodeposited In layer usually occurred on the Cu surface. A thermal process was adopted to turn Cu-In precursors into uniform Cu11In9 binary compounds. After deposition of the Se layer, annealing was employed to form chalcopyrite CIS. However, synthesis of CIS from Cu11In9 requires sufficient thermal energy. Annealing temperature and time were investigated to grow high quality CIS film. Various electrodeposition conditions were investigated to achieve the proper atomic ratio of CIS. The properties of the CIS films were characterized by scanning electron microscopy (SEM), X-ray Diffraction (XRD), and Raman spectra
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