8,797 research outputs found
Precoding-Based Network Alignment For Three Unicast Sessions
We consider the problem of network coding across three unicast sessions over
a directed acyclic graph, where each sender and the receiver is connected to
the network via a single edge of unit capacity. We consider a network model in
which the middle of the network only performs random linear network coding, and
restrict our approaches to precoding-based linear schemes, where the senders
use precoding matrices to encode source symbols. We adapt a precoding-based
interference alignment technique, originally developed for the wireless
interference channel, to construct a precoding-based linear scheme, which we
refer to as as a {\em precoding-based network alignment scheme (PBNA)}. A
primary difference between this setting and the wireless interference channel
is that the network topology can introduce dependencies between elements of the
transfer matrix, which we refer to as coupling relations, and can potentially
affect the achievable rate of PBNA. We identify all possible such coupling
relations, and interpret these coupling relations in terms of network topology
and present polynomial-time algorithms to check the presence of these coupling
relations. Finally, we show that, depending on the coupling relations present
in the network, the optimal symmetric rate achieved by precoding-based linear
scheme can take only three possible values, all of which can be achieved by
PBNA.Comment: arXiv admin note: text overlap with arXiv:1202.340
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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