1,451,625 research outputs found
A Relation Between Network Computation and Functional Index Coding Problems
In contrast to the network coding problem wherein the sinks in a network
demand subsets of the source messages, in a network computation problem the
sinks demand functions of the source messages. Similarly, in the functional
index coding problem, the side information and demands of the clients include
disjoint sets of functions of the information messages held by the transmitter
instead of disjoint subsets of the messages, as is the case in the conventional
index coding problem. It is known that any network coding problem can be
transformed into an index coding problem and vice versa. In this work, we
establish a similar relationship between network computation problems and a
class of functional index coding problems, viz., those in which only the
demands of the clients include functions of messages. We show that any network
computation problem can be converted into a functional index coding problem
wherein some clients demand functions of messages and vice versa. We prove that
a solution for a network computation problem exists if and only if a functional
index code (of a specific length determined by the network computation problem)
for a suitably constructed functional index coding problem exists. And, that a
functional index coding problem admits a solution of a specified length if and
only if a suitably constructed network computation problem admits a solution.Comment: 3 figures, 7 tables and 9 page
Recommended from our members
Enabling decentralized wireless index coding in practice
Index coding is a problem in theoretical computer science and network information theory that studies the optimal coding scheme for transmitting multiple messages across a network to receivers with different side information. The ultimate goal of index coding is to reduce transmission time in a communication network by minimizing the number of messages based on shared information. Index coding theory extends to several key engineering problems in network communication including peer to peer communication, distributed broadcast networks, and interference alignment. Although the theoretical connection between index coding and wireless networks is valuable, we focus on finding index coding strategies for a realistic wireless network. More specifically, we investigate how index coding can be applied to an OFDMA downlink network during the retransmission phase. An orthogonal frequency-division multiple access (OFDMA) downlink network is a network where data is sent downward from a designated higher-level transmitter to a group of receiving nodes. In addition, receivers can often decode the other receivers' physical layer signals on the other sub-channels that can be exploited as side information. If this side information is sent back to the transmitter, it can then be coded to cancel the interference in subsequent retransmission phases resulting in fewer retransmission messages. In this report, we explain the coding model and characterize the benefits of index coding for retransmissions within an OFDMA downlink network. In addition, we demonstrate the results of applying this index coding scheme in such network in both simulation and in an active wireless mesh network.Electrical and Computer Engineerin
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited input-output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. Radial basis function (RBF) networks are an important and popular network model for nonlinear system identification and dynamical modelling. A novel generalised multiscale RBF (MSRBF) network is introduced for Dst index modelling. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using an orthogonal least squares (OLS) type algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems
Distance, dissimilarity index, and network community structure
We address the question of finding the community structure of a complex
network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept
of network random walking is introduced and a distance measure defined. Here we
calculate, based on this distance measure, the dissimilarity index between
nearest-neighboring vertices of a network and design an algorithm to partition
these vertices into communities that are hierarchically organized. Each
community is characterized by an upper and a lower dissimilarity threshold. The
algorithm is applied to several artificial and real-world networks, and
excellent results are obtained. In the case of artificially generated random
modular networks, this method outperforms the algorithm based on the concept of
edge betweenness centrality. For yeast's protein-protein interaction network,
we are able to identify many clusters that have well defined biological
functions.Comment: 10 pages, 7 figures, REVTeX4 forma
Lobby index as a network centrality measure
We study the lobby index (l-index for short) as a local node centrality
measure for complex networks. The l-inde is compared with degree (a local
measure), betweenness and Eigenvector centralities (two global measures) in the
case of biological network (Yeast interaction protein-protein network) and a
linguistic network (Moby Thesaurus II). In both networks, the l-index has poor
correlation with betweenness but correlates with degree and Eigenvector. Being
a local measure, one can take advantage by using the l-index because it carries
more information about its neighbors when compared with degree centrality,
indeed it requires less time to compute when compared with Eigenvector
centrality. Results suggests that l-index produces better results than degree
and Eigenvector measures for ranking purposes, becoming suitable as a tool to
perform this task.Comment: 11 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1005.480
- …