Nokia (Portugal)

Modern metro and access optical networks, driven by 5G and fiber-to-the-home (FTTH), demand significantly higher capacity than current state-of-the-art direct-detection solutions can provide. Consequently, high-capacity coherent transceivers deployed, for example, in backbone and datacenter interconnects links, are now expanding into these new segments. However, unlike traditional long-haul links, the primary constraint in Passive Optical Networks (PONs) is the power budget. To ensure cost-effectiveness, efficiently scale the network, and reduce the need for in-line amplifiers, these systems may need to operate at elevated power levels to overcome distribution losses. However, this approach might increase the impact of fiber Kerr nonlinearities, which are typically negligible at lower launch power levels. This study addresses the resulting challenge of accurately modeling Nonlinear Interference (NLI) for short-reach links, which traditionally has been neglected. We validate a Monte-Carlo based Enhanced Gaussian Noise (EGN) model against Split-Step Fourier Method (SSFM) and the experimental measurements in 400G shortreach transmission links. The experimental results show a promising agreement (maximum deviation error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 0.30 dB) with the EGN model. This work confirms the EGN model as a robust and reliable tool for designing also next-generation short-reach networks, enabling more efficient network design by accounting for the interplay between NLI and transceiver noise characteristics.

The increasing complexity and capacity demands of modern optical communication networks require advanced tools for predictive maintenance and fault prevention. This study presents a systematic methodology for generating a comprehensive network failure database using Digital Twins (DTs) based on the open-source optical network simulation framework GNPy (Gaussian Noise model in Python). GNPy enables the generation of synthetic datasets to support the development of Machine Learning (ML) models for proactive fault detection and network performance optimization. By modeling a wide range of failure scenarios such as amplifier degradation, span loss variation, and component misconfigurations, GNPy enables detailed analysis of their impact on critical transmission parameters including Generalized Signal-to-Noise Ratio (GSNR), Optical Signal-to-Noise Ratio (OSNR), and channel power. The proposed methodology allows the generation of a diverse and scalable database that reflects both normal and degraded network operating conditions. This synthetic dataset provides valuable input for training ML algorithms to detect subtle performance degradations and predict potential failures before they impact running services. By leveraging this simulation-based data generation approach, network operators can significantly enhance the reliability and resilience of their transport networks. Furthermore, the integration of GNPy with data-driven ML models lays the foundation for intelligent network management systems capable of automated fault prediction and proactive maintenance strategies in future high-capacity optical networks.

We review four years of studies on various applications of coherent point-to-multipoint transceivers enabled by digital subcarrier multiplexing. We highlight the benefits of this approach to realize truly sustainable, scalable, and flexible optical networks. ©2025 The Author(s)

One of the main concerns for current multimedia platforms is the provisioning of content that provides a good Quality of Experience to end-users. This can be achieved through new interactive, personalized content applications, as well by improving the image quality delivered to the end-user. This chapter addresses these issues by describing mechanisms for changing content consumption. The aim is to give Application Service Providers (ASPs) new ways to allow users to configure contents according to their personal tastes while also improving their Quality of Experience, and to possibly charge users for such functionalities. The authors propose to employ computer vision techniques to produce extra object information, which further expands the range of video personalization possibilities on the presence of new video coding mechanisms1.

Due to lack of optical RAM buffers in fast optical switches (FOS), statistical multiplexing technologies using FOS like Optical Burst Switching (OBS) or Optical Packet Switching (OPS) are expected to have higher data losses than conventional electronic networks. Consequently, applications transferring data by means of TCP can suffer from a lower throughput. In this paper, we show that this low-throughput problem is mainly an artifact caused by the conventional TCP congestion control algorithms, and can be remedied by using a simple yet effective stop-and-wait congestion control algorithm instead, as long as the propagation delay between TCP source and TCP destination is small compared to the transmission time of an optical packet. We show that such a condition holds for a wide range of scenarios, including fat-tree-based data center networks. We also show that the throughput achieved in a FOS network using stop-and- wait can be the same as, or higher than that in an equivalent, conventional electronic network. I. INTRODUCTION

Voice over Internet Protocol (VoIP) applications based on peer-to-peer (P2P) communications have been experiencing considerable growth in terms of number of users. To overcome filtering policies or protect the privacy of their users, most of these applications implement mechanisms such as protocol obfuscation or payload encryption that avoid the inspection of their traffic, making it difficult to identify its nature. The incapacity to determine the application that is responsible for a certain flow raises challenges for the effective management of the network. In this paper, a new method for the identification of VoIP sessions is presented. The proposed mechanism classifies the flows, in real-time, based on the speech codec used in the session. To make the classification lightweight, the behavioral signatures for each analyzed codec were created using only the lengths of the packets. Unlike most previous approaches, the classifier does not use the lengths of the packets individually. Instead, it explores their level of heterogeneity in real time, using entropy to emphasize such feature. The results of the performance evaluation show that the proposed method is able to identify VoIP sessions accurately and simultaneously recognize the used speech codec.

In this letter, the performance of maximum-likelihood (ML) detection is evaluated and compared with the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers for a 3 × 158-Gb/s mode-division multiplexed dual-polarization quadrature phase shift keying orthogonal frequency division multiplexing transmission impaired by mode-dependent loss. The receiver schemes are compared in terms of performance and complexity. The simulations show that the ML approach outperforms both ZF and MMSE equalization for strongly coupled modes. However, the complexity of the ML detection is significantly higher and increases faster with the number of modes used for transmission and the modulation format order.

Relative-low-cost upgrade possibilities of realistic submarine links of &gt;4500km with 100G CP-QPSK transponders are analyzed with various formats. We showed that with the optimum pre-compensation, simpler NRZ-shaped CP-QPSK signal performs similarly to the RZ-shaped one.

We present a planning method to find the best compromise between spectral efficiency and 3R regenerator requirements by optimizing the assignment of guard-bands in the deployment of Nx100G superchannels.

We propose a novel wavelength assignment method for single-channel services in metropolitan networks that optimizes the spectrum to accommodate 100Gb/s Ethernet signals using multi-channel transceivers of limited wavelength-tuning properties.