Access and metro network convergence for flexible end-to-end network design

This paper reports on the architectural, protocol, physical layer, and integrated testbed demonstrations carried out by the DISCUS FP7 consortium in the area of access - metro network convergence. Our architecture modeling results show the vast potential for cost and power savings that node consolidation can bring. The architecture, however, also recognizes the limits of long-reach transmission for low-latency 5G services and proposes ways to address such shortcomings in future projects. The testbed results, which have been conducted end-to-end, across access - metro and core, and have targeted all the layers of the network from the application down to the physical layer, show the practical feasibility of the concepts proposed in the project

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Deflection Routing Strategies for Optical Burst Switching Networks: Contemporary Affirmation of the Recent Literature

A promising option to raising busty interchange in system communication could be Optical Burst Switched (OBS) networks among scalable and support routing effective. The routing schemes with disputation resolution got much interest, because the OBS network is buffer less in character. Because the deflection steering can use limited optical buffering or actually no buffering thus the choice or deflection routing techniques can be critical. Within this paper we investigate the affirmation of the current literature on alternate (deflection) routing strategies accessible for OBS networks

Single-ended differential protection in MTDC networks using optical sensors

This paper presents a method for rapid detection of faults on VSC multi-terminal HVDC transmission networks using multi-point optical current sensing. The proposed method uses differential protection as a guiding principle, and is implemented using current measurements obtained from optical current sensors distributed along the transmission line. Performance is assessed through detailed transient simulation using Matlab/Simulink® models, integrating inductive DC-line terminations, detailed DC circuit breaker models and a network of fiber-optic current sensors. Moreover, the feasibility and required performance of optical-based measurements is validated through laboratory testing. Simulation results demonstrate that the proposed protection algorithm can effectively, and within very short period of time, discriminate between faults on the protected line (internal faults), and those occurring on adjacent lines or busbars (external faults). Hardware tests prove that the scheme can be achieved with the existing, available sensing technology

Software defined networking: meeting carrier grade requirements

Software Defined Networking is a networking paradigm which allows network operators to manage networking elements using software running on an external server. This is accomplished by a split in the architecture between the forwarding element and the control element. Two technologies which allow this split for packet networks are ForCES and Openflow. We present energy efficiency and resilience aspects of carrier grade networks which can be met by Openflow. We implement flow restoration and run extensive experiments in an emulated carrier grade network. We show that Openflow can restore traffic quite fast, but its dependency on a centralized controller means that it will be hard to achieve 50 ms restoration in large networks serving many flows. In order to achieve 50 ms recovery, protection will be required in carrier grade networks

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

Future benefits and applications of intelligent on-board processing to VSAT services

The trends and roles of VSAT services in the year 2010 time frame are examined based on an overall network and service model for that period. An estimate of the VSAT traffic is then made and the service and general network requirements are identified. In order to accommodate these traffic needs, four satellite VSAT architectures based on the use of fixed or scanning multibeam antennas in conjunction with IF switching or onboard regeneration and baseband processing are suggested. The performance of each of these architectures is assessed and the key enabling technologies are identified

Flexible digital modulation and coding synthesis for satellite communications

An architecture and a hardware prototype of a flexible trellis modem/codec (FTMC) transmitter are presented. The theory of operation is built upon a pragmatic approach to trellis-coded modulation that emphasizes power and spectral efficiency. The system incorporates programmable modulation formats, variations of trellis-coding, digital baseband pulse-shaping, and digital channel precompensation. The modulation formats examined include (uncoded and coded) binary phase shift keying (BPSK), quatenary phase shift keying (QPSK), octal phase shift keying (8PSK), 16-ary quadrature amplitude modulation (16-QAM), and quadrature quadrature phase shift keying (Q squared PSK) at programmable rates up to 20 megabits per second (Mbps). The FTMC is part of the developing test bed to quantify modulation and coding concepts