2,046 research outputs found
Matrix-Monotonic Optimization for MIMO Systems
For MIMO systems, due to the deployment of multiple antennas at both the
transmitter and the receiver, the design variables e.g., precoders, equalizers,
training sequences, etc. are usually matrices. It is well known that matrix
operations are usually more complicated compared to their vector counterparts.
In order to overcome the high complexity resulting from matrix variables, in
this paper we investigate a class of elegant multi-objective optimization
problems, namely matrix-monotonic optimization problems (MMOPs). In our work,
various representative MIMO optimization problems are unified into a framework
of matrix-monotonic optimization, which includes linear transceiver design,
nonlinear transceiver design, training sequence design, radar waveform
optimization, the corresponding robust design and so on as its special cases.
Then exploiting the framework of matrix-monotonic optimization the optimal
structures of the considered matrix variables can be derived first. Based on
the optimal structure, the matrix-variate optimization problems can be greatly
simplified into the ones with only vector variables. In particular, the
dimension of the new vector variable is equal to the minimum number of columns
and rows of the original matrix variable. Finally, we also extend our work to
some more general cases with multiple matrix variables.Comment: 37 Pages, 5 figures, IEEE Transactions on Signal Processing, Final
Versio
Unified Joint Matrix-Monotonic Optimization of MIMO Training Sequences and Transceivers
Channel estimation and transmission constitute the most fundamental
functional modules of multiple-input multiple-output (MIMO) communication
systems. The underlying key tasks corresponding to these modules are training
sequence optimization and transceiver optimization. Hence, we jointly optimize
the linear transmit precoder and the training sequence of MIMO systems using
the metrics of their effective mutual information (MI), effective mean squared
error (MSE), effective weighted MI, effective weighted MSE, as well as their
effective generic Schur-convex and Schur-concave functions. Both statistical
channel state information (CSI) and estimated CSI are considered at the
transmitter in the joint optimization. A unified framework termed as joint
matrix-monotonic optimization is proposed. Based on this, the optimal precoder
matrix and training matrix structures can be derived for both CSI scenarios.
Then, based on the optimal matrix structures, our linear transceivers and their
training sequences can be jointly optimized. Compared to state-of-the-art
benchmark algorithms, the proposed algorithms visualize the bold explicit
relationships between the attainable system performance of our linear
transceivers conceived and their training sequences, leading to implementation
ready recipes. Finally, several numerical results are provided, which
corroborate our theoretical results and demonstrate the compelling benefits of
our proposed pilot-aided MIMO solutions.Comment: 29 pages, 7 figure
Hybrid Transceiver Optimization for Multi-Hop Communications
Multi-hop communication with the aid of large-scale antenna arrays will play
a vital role in future emergence communication systems. In this paper, we
investigate amplify-and-forward based and multiple-input multiple-output
assisted multi-hop communication, in which all nodes employ hybrid
transceivers. Moreover, channel errors are taken into account in our hybrid
transceiver design. Based on the matrix-monotonic optimization framework, the
optimal structures of the robust hybrid transceivers are derived. By utilizing
these optimal structures, the optimizations of analog transceivers and digital
transceivers can be separated without loss of optimality. This fact greatly
simplifies the joint optimization of analog and digital transceivers. Since the
optimization of analog transceivers under unit-modulus constraints is
non-convex, a projection type algorithm is proposed for analog transceiver
optimization to overcome this difficulty. Based on the derived analog
transceivers, the optimal digital transceivers can then be derived using
matrix-monotonic optimization. Numeral results obtained demonstrate the
performance advantages of the proposed hybrid transceiver designs over other
existing solutions.Comment: 32 pages, 6 figures. This manuscript has been submitted to IEEE
Journal on Selected Areas in Communications (special issue on Multiple
Antenna Technologies for Beyond 5G
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
The characterization of the global maximum of energy efficiency (EE) problems
in wireless networks is a challenging problem due to the non-convex nature of
investigated problems in interference channels. The aim of this work is to
develop a new and general framework to achieve globally optimal solutions.
First, the hidden monotonic structure of the most common EE maximization
problems is exploited jointly with fractional programming theory to obtain
globally optimal solutions with exponential complexity in the number of network
links. To overcome this issue, we also propose a framework to compute
suboptimal power control strategies characterized by affordable complexity.
This is achieved by merging fractional programming and sequential optimization.
The proposed monotonic framework is used to shed light on the ultimate
performance of wireless networks in terms of EE and also to benchmark the
performance of the lower-complexity framework based on sequential programming.
Numerical evidence is provided to show that the sequential fractional
programming framework achieves global optimality in several practical
communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal
Processin
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Sum-Rate Maximization in Two-Way AF MIMO Relaying: Polynomial Time Solutions to a Class of DC Programming Problems
Sum-rate maximization in two-way amplify-and-forward (AF) multiple-input
multiple-output (MIMO) relaying belongs to the class of difference-of-convex
functions (DC) programming problems. DC programming problems occur as well in
other signal processing applications and are typically solved using different
modifications of the branch-and-bound method. This method, however, does not
have any polynomial time complexity guarantees. In this paper, we show that a
class of DC programming problems, to which the sum-rate maximization in two-way
MIMO relaying belongs, can be solved very efficiently in polynomial time, and
develop two algorithms. The objective function of the problem is represented as
a product of quadratic ratios and parameterized so that its convex part (versus
the concave part) contains only one (or two) optimization variables. One of the
algorithms is called POlynomial-Time DC (POTDC) and is based on semi-definite
programming (SDP) relaxation, linearization, and an iterative search over a
single parameter. The other algorithm is called RAte-maximization via
Generalized EigenvectorS (RAGES) and is based on the generalized eigenvectors
method and an iterative search over two (or one, in its approximate version)
optimization variables. We also derive an upper-bound for the optimal values of
the corresponding optimization problem and show by simulations that this
upper-bound can be achieved by both algorithms. The proposed methods for
maximizing the sum-rate in the two-way AF MIMO relaying system are shown to be
superior to other state-of-the-art algorithms.Comment: 35 pages, 10 figures, Submitted to the IEEE Trans. Signal Processing
in Nov. 201
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