1,975 research outputs found
Delay Reduction in Multi-Hop Device-to-Device Communication using Network Coding
This paper considers the problem of reducing the broadcast decoding delay of
wireless networks using instantly decodable network coding (IDNC) based
device-to-device (D2D) communications. In a D2D configuration, devices in the
network can help hasten the recovery of the lost packets of other devices in
their transmission range by sending network coded packets. Unlike previous
works that assumed fully connected network, this paper proposes a partially
connected configuration in which the decision should be made not only on the
packet combinations but also on the set of transmitting devices. First, the
different events occurring at each device are identified so as to derive an
expression for the probability distribution of the decoding delay. The joint
optimization problem over the set of transmitting devices and the packet
combinations of each is, then, formulated. The optimal solution of the joint
optimization problem is derived using a graph theory approach by introducing
the cooperation graph and reformulating the problem as a maximum weight clique
problem in which the weight of each vertex is the contribution of the device
identified by the vertex. Through extensive simulations, the decoding delay
experienced by all devices in the Point to Multi-Point (PMP) configuration, the
fully connected D2D (FC-D2D) configuration and the more practical partially
connected D2D (PC-D2D) configuration are compared. Numerical results suggest
that the PC-D2D outperforms the FC-D2D and provides appreciable gain especially
for poorly connected networks
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Optimization Framework and Graph-Based Approach for Relay-Assisted Bidirectional OFDMA Cellular Networks
This paper considers a relay-assisted bidirectional cellular network where
the base station (BS) communicates with each mobile station (MS) using OFDMA
for both uplink and downlink. The goal is to improve the overall system
performance by exploring the full potential of the network in various
dimensions including user, subcarrier, relay, and bidirectional traffic. In
this work, we first introduce a novel three-time-slot time-division duplexing
(TDD) transmission protocol. This protocol unifies direct transmission, one-way
relaying and network-coded two-way relaying between the BS and each MS. Using
the proposed three-time-slot TDD protocol, we then propose an optimization
framework for resource allocation to achieve the following gains: cooperative
diversity (via relay selection), network coding gain (via bidirectional
transmission mode selection), and multiuser diversity (via subcarrier
assignment). We formulate the problem as a combinatorial optimization problem,
which is NP-complete. To make it more tractable, we adopt a graph-based
approach. We first establish the equivalence between the original problem and a
maximum weighted clique problem in graph theory. A metaheuristic algorithm
based on any colony optimization (ACO) is then employed to find the solution in
polynomial time. Simulation results demonstrate that the proposed protocol
together with the ACO algorithm significantly enhances the system total
throughput.Comment: 27 pages, 8 figures, 2 table
Next Generation Cluster Editing
This work aims at improving the quality of structural variant prediction from
the mapped reads of a sequenced genome. We suggest a new model based on cluster
editing in weighted graphs and introduce a new heuristic algorithm that allows
to solve this problem quickly and with a good approximation on the huge graphs
that arise from biological datasets
Delivery Time Reduction for Order-Constrained Applications using Binary Network Codes
Consider a radio access network wherein a base-station is required to deliver
a set of order-constrained messages to a set of users over independent erasure
channels. This paper studies the delivery time reduction problem using
instantly decodable network coding (IDNC). Motivated by time-critical and
order-constrained applications, the delivery time is defined, at each
transmission, as the number of undelivered messages. The delivery time
minimization problem being computationally intractable, most of the existing
literature on IDNC propose sub-optimal online solutions. This paper suggests a
novel method for solving the problem by introducing the delivery delay as a
measure of distance to optimality. An expression characterizing the delivery
time using the delivery delay is derived, allowing the approximation of the
delivery time minimization problem by an optimization problem involving the
delivery delay. The problem is, then, formulated as a maximum weight clique
selection problem over the IDNC graph wherein the weight of each vertex
reflects its corresponding user and message's delay. Simulation results suggest
that the proposed solution achieves lower delivery and completion times as
compared to the best-known heuristics for delivery time reduction
RASCAL: calculation of graph similarity using maximum common edge subgraphs
A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a minimum similarity threshold, the procedure consists of an initial screening process to determine whether it is possible for the measure of similarity between the two graphs to exceed the minimum threshold, followed by a rigorous maximum common edge subgraph (MCES) detection algorithm to compute the exact degree and composition of similarity. The proposed MCES algorithm is based on a maximum clique formulation of the problem and is a significant improvement over other published algorithms. It presents new approaches to both lower and upper bounding as well as vertex selection
Structured learning of sum-of-submodular higher order energy functions
Submodular functions can be exactly minimized in polynomial time, and the
special case that graph cuts solve with max flow \cite{KZ:PAMI04} has had
significant impact in computer vision
\cite{BVZ:PAMI01,Kwatra:SIGGRAPH03,Rother:GrabCut04}. In this paper we address
the important class of sum-of-submodular (SoS) functions
\cite{Arora:ECCV12,Kolmogorov:DAM12}, which can be efficiently minimized via a
variant of max flow called submodular flow \cite{Edmonds:ADM77}. SoS functions
can naturally express higher order priors involving, e.g., local image patches;
however, it is difficult to fully exploit their expressive power because they
have so many parameters. Rather than trying to formulate existing higher order
priors as an SoS function, we take a discriminative learning approach,
effectively searching the space of SoS functions for a higher order prior that
performs well on our training set. We adopt a structural SVM approach
\cite{Joachims/etal/09a,Tsochantaridis/etal/04} and formulate the training
problem in terms of quadratic programming; as a result we can efficiently
search the space of SoS priors via an extended cutting-plane algorithm. We also
show how the state-of-the-art max flow method for vision problems
\cite{Goldberg:ESA11} can be modified to efficiently solve the submodular flow
problem. Experimental comparisons are made against the OpenCV implementation of
the GrabCut interactive segmentation technique \cite{Rother:GrabCut04}, which
uses hand-tuned parameters instead of machine learning. On a standard dataset
\cite{Gulshan:CVPR10} our method learns higher order priors with hundreds of
parameter values, and produces significantly better segmentations. While our
focus is on binary labeling problems, we show that our techniques can be
naturally generalized to handle more than two labels
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