I. de Miguel

Bio: I. de Miguel is an academic researcher from University of Valladolid. The author has contributed to research in topics: Network topology & Logical topology. The author has an hindex of 13, co-authored 50 publications receiving 527 citations. Previous affiliations of I. de Miguel include ETSI & University College London.

Next-generation 100-gigabit metro ethernet (100 GbME) using multiwavelength optical rings

Abstract: This paper investigates the challenges for developing the current local area network (LAN)-based Ethernet protocol into a technology for future network architectures that is capable of satisfying dynamic traffic demands with hard service guarantees using high-bit-rate channels (80...100 Gb/s). The objective is to combine high-speed optical transmission and physical interfaces (PHY) with a medium access control (MAC) protocol, designed to meet the service guarantees in future metropolitan-area networks (MANs). Ethernet is an ideal candidate for the extension into the MAN as it allows seamless compatibility with the majority of existing LANs. The proposed extension of the MAC protocol focuses on backward compatibility as well as on the exploitation of the wavelength domain for routing of variable traffic demands. The high bit rates envisaged will easily exhaust the capacity of a single optical fiber in the C band and will require network algorithms optimizing the reuse of wavelength resources. To investigate this, four different static and dynamic optical architectures were studied that potentially offer advantages over current link-based designs. Both analytical and numerical modeling techniques were applied to quantify and compare the network performance for all architectures in terms of achievable throughput, delay, and the number of required wavelengths and to investigate the impact of nonuniform traffic demands. The results show that significant resource savings can be achieved by using end-to-end dynamic lightpath allocation, but at the expense of high delay.

A Cognitive Quality of Transmission Estimator for Core Optical Networks

Abstract: We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.

PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks

Abstract: In this paper, a proportional-integral-derivative (PID) controller integrated with a neural network (NN) is proposed to ensure quality of service (QoS) bandwidth requirements in passive optical networks (PONs). To the best of our knowledge, this is the first time an approach that implements aNNto tune a PID to dealwithQoS in PONs is used. In contrast to other tuning techniques such as Ziegler– Nichols or genetic algorithms (GA), our proposal allows a real-time adjustment of the tuning parameters according to the network conditions. Thus, the new algorithm provides an online control of the tuning process unlike the ZN and GA techniques, whose tuning parameters are calculated offline. The algorithm, called neural network service level PID (NNSPID), guarantees minimum bandwidth levels to users depending on their service level agreement, and it is compared with a tuning technique based on genetic algorithms (GASPID). The simulation study demonstrates that NN-SPID continuously adapts the tuning parameters, achieving lower fluctuations than GA-SPID in the allocation process. As a consequence, it provides a more stable response than GA-SPID since it needs to launch the GA to obtain new tuning values. Furthermore, NN-SPID guarantees the minimum bandwidth levels faster than GA-SPID. Finally, NN-SPID is more robust than GA-SPID under real-time changes of the guaranteed bandwidth levels, as GA-SPID shows high fluctuations in the allocated bandwidth, especially just after any change is made.

Cognitive dynamic optical networks

Abstract: Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project.

An Overview on Application of Machine Learning Techniques in Optical Networks

Abstract: 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 this paper proposing new possible research directions.

Analysis of a dynamically wavelength-routed optical burst switched network architecture

Abstract: The concept of optical burst switching (OBS) aims to allow access to optical bandwidth in dense wavelength division multiplexed (DWDM) networks at fractions of the optical line rate to improve bandwidth utilization efficiency. This paper studies an alternative network architecture combining OBS with dynamic wavelength allocation under fast circuit switching to provide a scalable optical architecture with a guaranteed QoS in the presence of dynamic and bursty traffic loads. In the proposed architecture, all processing and buffering are concentrated at the network edge and bursts are routed over an optical transport core using dynamic wavelength assignment. It is assumed that there are no buffers or wavelength conversion in core nodes and that fast tuneable laser sources are used in the edge routers. This eliminates the forwarding bottleneck of electronic routers in DWDM networks for terabit-per-second throughput and guarantees forwarding with predefined delay at the edge and latency due only to propagation time in the core. The edge burst aggregation mechanisms are evaluated for a range of traffic statistics to identify their impact on the allowable burst lengths, required buffer size and achievable edge delays. Bandwidth utilization and wavelength reuse are introduced as new parameters characterizing the network performance in the case of dynamic wavelength allocation. Based on an analytical model, upper bounds for these parameters are derived to quantify the advantages of wavelength channel reuse, including the influence of the signaling round-trip time required for lightpath reservation. The results allow to quantify the operational gain achievable with fast wavelength switching compared to quasistatic wavelength-routed optical networks and can be applied to the design of future optical network architectures.

Toward an Efficient C-RAN Optical Fronthaul for the Future Networks: A Tutorial on Technologies, Requirements, Challenges, and Solutions

Abstract: The exponential traffic growth, demand for high speed wireless data communications, as well as incessant deployment of innovative wireless technologies, services, and applications, have put considerable pressure on the mobile network operators (MNOs). Consequently, cellular access network performance in terms of capacity, quality of service, and network coverage needs further considerations. In order to address the challenges, MNOs, as well as equipment vendors, have given significant attention to the small-cell schemes based on cloud radio access network (C-RAN). This is due to its beneficial features in terms of performance optimization, cost-effectiveness, easier infrastructure deployment, and network management. Nevertheless, the C-RAN architecture imposes stringent requirements on the fronthaul link for seamless connectivity. Digital radio over fiber-based common public radio interface (CPRI) is the fundamental means of distributing baseband samples in the C-RAN fronthaul. However, optical links which are based on CPRI have bandwidth and flexibility limitations. Therefore, these limitations might constrain or make them impractical for the next generation mobile systems which are envisaged not only to support carrier aggregation and multi-band but also envisioned to integrate technologies like millimeter-wave (mm-wave) and massive multiple-input multiple-output antennas into the base stations. In this paper, we present comprehensive tutorial on technologies, requirements, architectures, challenges, and proffer potential solutions on means of achieving an efficient C-RAN optical fronthaul for the next-generation network such as the fifth generation network and beyond. A number of viable fronthauling technologies such as mm-wave and wireless fidelity are considered and this paper mainly focuses on optical technologies such as optical fiber and free-space optical. We also present feasible means of reducing the system complexity, cost, bandwidth requirement, and latency in the fronthaul. Furthermore, means of achieving the goal of green communication networks through reduction in the power consumption by the system are considered.

Optical Performance Monitoring: A Review of Current and Future Technologies

Abstract: Optical performance monitoring (OPM) is the estimation and acquisition of different physical parameters of transmitted signals and various components of an optical network. OPM functionalities are indispensable in ensuring robust network operation and plays a key role in enabling flexibility and improve overall network efficiency. We review the development of various OPM techniques for direct-detection systems and digital coherent systems and discuss future OPM challenges in flexible and elastic optical networks.