22,380 research outputs found
Guest Editorial: Nonlinear Optimization of Communication Systems
Linear programming and other classical optimization techniques have found important applications in communication systems for many decades. Recently, there has been a surge in research activities that utilize the latest developments in nonlinear optimization to tackle a much wider scope of work in the analysis and design of communication systems. These activities involve every “layer” of the protocol stack and the principles of layered network architecture itself, and have made intellectual and practical impacts significantly beyond the established frameworks of optimization of communication systems in the early 1990s. These recent results are driven by new demands in the areas of communications and networking, as well as new tools emerging from optimization theory. Such tools include the powerful theories and highly efficient computational algorithms for nonlinear convex optimization, together with global solution methods and relaxation techniques for nonconvex optimization
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Distortion Minimization in Gaussian Layered Broadcast Coding with Successive Refinement
A transmitter without channel state information (CSI) wishes to send a
delay-limited Gaussian source over a slowly fading channel. The source is coded
in superimposed layers, with each layer successively refining the description
in the previous one. The receiver decodes the layers that are supported by the
channel realization and reconstructs the source up to a distortion. The
expected distortion is minimized by optimally allocating the transmit power
among the source layers. For two source layers, the allocation is optimal when
power is first assigned to the higher layer up to a power ceiling that depends
only on the channel fading distribution; all remaining power, if any, is
allocated to the lower layer. For convex distortion cost functions with convex
constraints, the minimization is formulated as a convex optimization problem.
In the limit of a continuum of infinite layers, the minimum expected distortion
is given by the solution to a set of linear differential equations in terms of
the density of the fading distribution. As the bandwidth ratio b (channel uses
per source symbol) tends to zero, the power distribution that minimizes
expected distortion converges to the one that maximizes expected capacity.
While expected distortion can be improved by acquiring CSI at the transmitter
(CSIT) or by increasing diversity from the realization of independent fading
paths, at high SNR the performance benefit from diversity exceeds that from
CSIT, especially when b is large.Comment: Accepted for publication in IEEE Transactions on Information Theor
Estimation Diversity and Energy Efficiency in Distributed Sensing
Distributed estimation based on measurements from multiple wireless sensors
is investigated. It is assumed that a group of sensors observe the same
quantity in independent additive observation noises with possibly different
variances. The observations are transmitted using amplify-and-forward (analog)
transmissions over non-ideal fading wireless channels from the sensors to a
fusion center, where they are combined to generate an estimate of the observed
quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by
the fusion center, the equal-power transmission strategy is first discussed,
where the system performance is analyzed by introducing the concept of
estimation outage and estimation diversity, and it is shown that there is an
achievable diversity gain on the order of the number of sensors. The optimal
power allocation strategies are then considered for two cases: minimum
distortion under power constraints; and minimum power under distortion
constraints. In the first case, it is shown that by turning off bad sensors,
i.e., sensors with bad channels and bad observation quality, adaptive power
gain can be achieved without sacrificing diversity gain. Here, the adaptive
power gain is similar to the array gain achieved in Multiple-Input
Single-Output (MISO) multi-antenna systems when channel conditions are known to
the transmitter. In the second case, the sum power is minimized under
zero-outage estimation distortion constraint, and some related energy
efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin
MIMO Networks: the Effects of Interference
Multiple-input/multiple-output (MIMO) systems promise enormous capacity
increase and are being considered as one of the key technologies for future
wireless networks. However, the decrease in capacity due to the presence of
interferers in MIMO networks is not well understood. In this paper, we develop
an analytical framework to characterize the capacity of MIMO communication
systems in the presence of multiple MIMO co-channel interferers and noise. We
consider the situation in which transmitters have no information about the
channel and all links undergo Rayleigh fading. We first generalize the known
determinant representation of hypergeometric functions with matrix arguments to
the case when the argument matrices have eigenvalues of arbitrary multiplicity.
This enables the derivation of the distribution of the eigenvalues of Gaussian
quadratic forms and Wishart matrices with arbitrary correlation, with
application to both single user and multiuser MIMO systems. In particular, we
derive the ergodic mutual information for MIMO systems in the presence of
multiple MIMO interferers. Our analysis is valid for any number of interferers,
each with arbitrary number of antennas having possibly unequal power levels.
This framework, therefore, accommodates the study of distributed MIMO systems
and accounts for different positions of the MIMO interferers.Comment: Submitted to IEEE Trans. on Info. Theor
Eigenvalue Dynamics of a Central Wishart Matrix with Application to MIMO Systems
We investigate the dynamic behavior of the stationary random process defined
by a central complex Wishart (CW) matrix as it varies along a
certain dimension . We characterize the second-order joint cdf of the
largest eigenvalue, and the second-order joint cdf of the smallest eigenvalue
of this matrix. We show that both cdfs can be expressed in exact closed-form in
terms of a finite number of well-known special functions in the context of
communication theory. As a direct application, we investigate the dynamic
behavior of the parallel channels associated with multiple-input
multiple-output (MIMO) systems in the presence of Rayleigh fading. Studying the
complex random matrix that defines the MIMO channel, we characterize the
second-order joint cdf of the signal-to-noise ratio (SNR) for the best and
worst channels. We use these results to study the rate of change of MIMO
parallel channels, using different performance metrics. For a given value of
the MIMO channel correlation coefficient, we observe how the SNR associated
with the best parallel channel changes slower than the SNR of the worst
channel. This different dynamic behavior is much more appreciable when the
number of transmit () and receive () antennas is similar. However, as
is increased while keeping fixed, we see how the best and worst
channels tend to have a similar rate of change.Comment: 15 pages, 9 figures and 1 table. This work has been accepted for
publication at IEEE Trans. Inf. Theory. Copyright (c) 2014 IEEE. Personal use
of this material is permitted. However, permission to use this material for
any other purposes must be obtained from the IEEE by sending a request to
[email protected]
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