Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of conventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing next-generation intelligent OPM systems.

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler.

Robustness against the large linewidth semiconductor laser-induced impairments in coherent systems is experimentally demonstrated for a feedforward neural network-enabled EVM estimation scheme. A mean error of 0.4% is achieved for 28 Gbaud square and circular QAM signals and linewidths up to 12.3 MHz. 

The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. We experimentally demonstrate the effects of signal insertion attacks with varying intensity in a real-life scenario. By applying data analytics tools, we analyze the properties of the obtained dataset to determine how the relationships among different optical performance monitoring (OPM) parameters of the signal change in the presence of an attack as opposed to the normal operating conditions. In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. We demonstrate the potential and the challenges of unsupervised learning for attack detection, propose guidelines for attack signature identification needed for the detection of the considered attack methods, and discuss remaining challenges related to optical network security.

Content Delivery Networks (CDNs) are crucial for enabling delivery of services that require high capacity and low latency, primarily through geographically-diverse content replication. Optical networks are the only available future-proof technology that meets the reach and capacity requirements of CDNs. However, the underlying physical network infrastructure is vulnerable to various security threats, and the increasing importance of CDNs in supporting vital services intensifies the concerns related to their robustness. Malicious attackers can target critical network elements, thus severely degrading network connectivity and causing large-scale service disruptions. One way in which network operators and cloud computing providers can increase the robustness against malicious attacks is by changing the topological properties of the network through infrastructure upgrades. This work proposes a framework for CDN infrastructure upgrade that performs sparse link and replica addition with the objective of maximizing the content accessibility under targeted link cut attacks. The framework is based on a newly defined content accessibility metric denoted as mu-ACA which allows the network operator to gauge the CDN robustness over a range of attacks with varying intensity. Two heuristics, namely Content-Accessibility Aware Link Addition Heuristic (CAA-LAH), and Content-Accessibility-Aware Replica Addition Heuristic (CAA-RAH) are developed to perform strategic link and replica placement, respectively, and hamper attackers from disconnecting users from the content even in severe attack scenarios. Extensive experiments on real-world reference network topologies show that the proposed framework effectively increases the CDN robustness by adding a few links or replicas to the network.

Filterless optical networks based on broadcast-and-select nodes and coherent transceivers are attractive cost-effective and flexible solutions in core networks. In this paper, we explore the suitability of filterless architectures in metropolitan core and aggregation networks.

Communication networks are embracing the software defined networking (SDN) paradigm. Its architectural shift assumes that a remote SDN controller (SDNC) in the control plane is responsible for configuring the underlying devices of the forwarding plane. In order to support flexibility-motivated network slicing, SDN-based networks employ another entity in the control plane, a network hypervisor (NH). This paper first discusses different protection strategies for the control plane with NHs and presents the corresponding availability models, which assume possible failures of links and nodes in the forwarding plane and the control plane. An analysis of these protection alternatives is then performed so as to compare average control plane availability, average path length for the control communication that traverses NH, and infrastructure resources required to support them. Our results confirm the intuition that the NH introduction generally results in a reduction of the control plane availability, which stresses the need for appropriate protection. However, the availability achieved by each of the considered strategies is impacted differently by the node availability and the link failure probability, thus calling for a careful selection that is based on the infrastructure features.

Wireless network solutions, a dominant enabling technology for the backhaul segment, are susceptible to weather disturbances that can substantially degrade network throughput and/or delay, compromising the stringent 5G requirements. These effects can be alleviated by centralized rerouting realized by software defined networking architecture. However, careless frequent reconfigurations can lead to inconsistencies in the network states due to asynchrony between different switches, which can create congestion and limit the rerouting gain. The aim of this paper is to minimize the total data loss during rain disturbance by proposing an algorithm that decides on the timing, the sequence, and the paths for rerouting of network flows considering the imposed congestion during reconfiguration. At each time sample, the central controller decides whether to adopt the optimal routes at a switching cost, defined as the imposed congestion, or to keep using existing, sub-optimal routes at a throughput loss. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of rain attenuation for the whole rain period. For dynamic scenarios with unknown future rain attenuation, we propose an online consistency-aware rerouting algorithm, called consistency-aware rerouting with prediction (CARP), which uses the temporal correlation of rain fading to estimate future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our CARP algorithm, substantially reducing data loss and increasing network throughput with a fewer number of rerouting actions compared to a greedy and a regular rerouting benchmarking approaches.