1,309 research outputs found
Optical Networks and Interconnects
The rapid evolution of communication technologies such as 5G and beyond, rely
on optical networks to support the challenging and ambitious requirements that
include both capacity and reliability. This chapter begins by giving an
overview of the evolution of optical access networks, focusing on Passive
Optical Networks (PONs). The development of the different PON standards and
requirements aiming at longer reach, higher client count and delivered
bandwidth are presented. PON virtualization is also introduced as the
flexibility enabler. Triggered by the increase of bandwidth supported by access
and aggregation network segments, core networks have also evolved, as presented
in the second part of the chapter. Scaling the physical infrastructure requires
high investment and hence, operators are considering alternatives to optimize
the use of the existing capacity. This chapter introduces different planning
problems such as Routing and Spectrum Assignment problems, placement problems
for regenerators and wavelength converters, and how to offer resilience to
different failures. An overview of control and management is also provided.
Moreover, motivated by the increasing importance of data storage and data
processing, this chapter also addresses different aspects of optical data
center interconnects. Data centers have become critical infrastructure to
operate any service. They are also forced to take advantage of optical
technology in order to keep up with the growing capacity demand and power
consumption. This chapter gives an overview of different optical data center
network architectures as well as some expected directions to improve the
resource utilization and increase the network capacity
FlexGrid optical network simulator implementation
A study about EON has been realized. Firstly, the capacity and fragmentation of EON network has been studied; study published in (Jaume, Xavier y Gabriel, Efficient spectrum assignment in Elastic Optical Networks 2016). Then, a study about the measurement of transceivers of EON was done. Also, different configurations have been compared to establish the connections in the network in order to improve the spectral efficiency. Finally, how the connections are adapted better in the network when the bandwidth of each connection increases has been studied
A branch-and-cut algorithm for the routing and spectrum allocation problem
One of the most promising solutions to deal with huge data traffic demands in
large communication networks is given by flexible optical networking, in particular
the flexible grid (flexgrid) technology specified in the ITU-T standard
G.694.1. In this specification, the frequency spectrum of an optical fiber link is
divided into narrow frequency slots. Any sequence of consecutive slots can be
used as a simple channel, and such a channel can be switched in the network
nodes to create a lightpath. In this kind of networks, the problem of establishing
lightpaths for a set of end-to-end demands that compete for spectrum resources
is called the routing and spectrum allocation problem (RSA). Due to its relevance,
RSA has been intensively studied in the last years. It has been shown
to be NP-hard and different solution approaches have been proposed for this
problem. In this paper we present several families of valid inequalities, valid
equations, and optimality cuts for a natural integer programming formulation
of RSA and, based on these results, we develop a branch-and-cut algorithm for
this problem. Our computational experiments suggest that such an approach is
effective at tackling this problem
Channel allocation in elastic optical networks using traveling salesman problem algorithms
Elastic optical networks have been proposed to support high data rates in metro and core networks. However, frequency allocation of the channels (i.e., channel ordering) in such networks is a challenging problem. This requires arranging the optical channels within the frequency grid with the objective of ensuring a minimum signal-to-noise ratio (SNR). An optimal arrangement results in the highest SNR margin for the entire network. However, determining the optimal arrangement requires an exhaustive search through all possible arrangements (permutations) of the channels. The search space increases exponentially with the number of channels. This discourages an algorithm employing an exhaustive search for the optimal frequency allocation. We utilize the Gaussian noise (GN) model to formulate the frequency allocation (channel ordering) problem as a variant of the traveling salesman problem (TSP) using graph theory. Thereafter, we utilize graph-theoretic tools for the TSP from the existing literature to solve the channel ordering problem. Performance figures obtained for the proposed scheme show that it is marginally inferior to the optimal search (through all possible permutations) and outperforms any random allocation scheme. Moreover, the proposed scheme is implementable for a scenario with a large number of channels. In comparison, an exhaustive search with the GN model and split-step Fourier method simulations are shown to be feasible for a small number of channels only. It is also illustrated that the SNR decreases with an increase in bandwidth when the frequency separation is high
Software Defined Applications in Cellular and Optical Networks
abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Hybrid 5G optical-wireless SDN-based networks, challenges and open issues
The fifth-generation (5G) mobile networks are expected to bring higher capacity, higher density of mobile devices, lower battery consumption and improved coverage. 5G entails the convergence of wireless and wired communications in a unified and efficient architecture. Mobile nodes, as defined in fourth-generation era, are transformed in heterogeneous networks to make the front-haul wireless domains flexible and intelligent. This work highlights a set of critical challenges in advancing 5G networks, fuelled by the utilisation of the network function virtualisation, the software defined radio and the software defined networks techniques. Furthermore, a novel conceptual model is presented in terms of control and management planes, where the inner architectural components are introduced in detail
Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks
This paper proposes a distributed collaborative learning approach for cognitive and autonomous multi-domain elastic optical networking (EON). The proposed approach exploits a knowledge-defined networking framework which leverages a broker plane to coordinate the operations of multiple EON domains and applies machine learning (ML) to support autonomous and cognitive inter-domain service provisioning. By employing multiple distributed ML blocks learning domain-level features and working with broker plane aggregation ML blocks (through the chain rule-based training), the proposed approach enables to develop cognitive networking applications that can fully exploit the multi-domain EON states while obviating the need for the raw and confidential intra-domain data. In particular, we investigate end-to-end quality-of-transmission estimation application using the distributed learning approach and propose three estimator designs incorporating the concepts of multi-task learning (MTL) and transfer learning (TL). Evaluations with experimental data demonstrate that the proposed designs can achieve estimation accuracies very close to (with differences less than 0.5%) or even higher than (with MTL/TL) those of the baseline models assuming full domain visibility
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