Nokia (United States)

Photonic neural networks can significantly boost the performance of their digital electronic counterparts by benefiting from large optical bandwidth, low-loss signal transmission, and various multiplexing schemes. Hence, high accuracy and robust training and inference in such networks are critical for their large-scale deployment. Here, we demonstrate integrated end-to-end photonic deep neural networks that offer scalability, low-latency signal processing, and robust and repeatable end-to-end backpropagation training. Moreover, we use standard silicon photonic platforms which enable high-yield and low-cost fabrication of large-scale photonic neural networks for the next generation of deep learning systems.

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)

We demonstrate downstream and burst upstream operation of 120-GBd NRZ VHSP using single bidirectional bismuth-doped fiber amplifier at OLT, acting simultaneously as booster and preamplifier. We investigate SOA power leveling for dynamic range reduction and output gating. >35-dB optical budget and 20-km transmission are shown. ©2025 The Author(s)

We detect the recent M8.8 mega-earthquake in Eastern Russia, on a 4400 km long active telecom cable in the Pacific Ocean. The resolution achieved 100 m represents the highest spatial reso-lution, the largest number of ocean-bottom sensors, and the first fiber-optic deep-ocean observation of a tsunami wave.

In recent years, the radio access network (RAN) domain has seen significant changes with increased virtualization and softwarization, driven by the Open RAN (O-RAN) movement. However, the fundamental building block of the cellular network, i.e., the base station, remains unchanged and ill-equipped to handle this architectural evolution. In particular, there exists a general lack of programmability and composability along with a protocol stack that grapples with the intricacies of the 3GPP and O-RAN specifications. Recognizing the need for an “O-RAN-native” approach to base station design, this paper introduces HexRAN– a novel base station architecture characterized by key features relating to RAN disaggregation and composability, 3GPP and O-RAN protocol integration and programmability, robust controller interactions, and customizable RAN slicing. Furthermore, the paper also includes a concrete systems-level prototype and comprehensive experimental evaluation of HexRAN on an over-the-air testbed. The results demonstrate that HexRAN uses only 8% more computing resources compared to the baseline, while managing twice the user plane traffic, delivering control plane processing latency of under 120μs, and achieving 100% processing reliability. This underscores the scalability and performance advantages of the proposed architecture.

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.

Backscatter power measurements are collected to characterize indoor radar clutter in monostatic sensing applications. A narrowband 28 GHz sounder used a quasi-monostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth. Power variation in azimuth around the local average is found to be within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 ~ d B}$</tex> of a lognormal distribution with a standard deviation of 6.8 dB. Backscatter azimuth spectra are found to be highly variable with location, with cross-correlation coefficients on the order of 0.3 at separations as small as 0.1 m. These statistics are needed for system-level evaluation of RF sensing performance.

Forecasting state of polarization (SOP) in aerial fibers poses several challenges due to exposure to dynamic und unpredictable environmental factors, such as wind-induced vibrations, leading to multi-timescale SOP variations. Traditional statistical and deterministic approaches may struggle to accurately model the complex, nonlinear, and multi-scale dynamics of these SOP fluctuations, making them less effective for real-world scenarios. To address these challenges, this study proposes a weather-adaptive forecasting framework combining discrete wavelet transform and artificial neural networks to predict SOP changes across varying timescales. By integrating environmental data, the model effectively captures rapid fluctuations and gradual drifts, significantly improving forecasting accuracy. Validation using field data across two locations demonstrates the model's reliability for both short-term and long-term predictions. Including environmental factors namely wind speed, temperature, and humidity improves long-term forecasting accuracy by over 64.5% compared to models taking as input SOP change data alone. Wind gusts enhance short-term forecasting under extremely windy conditions. Our proposed framework outperforms conventional techniques, offering a reliable framework for SOP change prediction and advancing proactive optical system monitoring.

Mixed reality (MR) telepresence provides a shared common 3D space for networked users to enjoy real-time immersive experiences with natural interactivity and movement. Due to its high hardware and compute demands, a large-scale cloud-edge MR solution is necessary for achieving critical mass adoption, where users engage in the MR environment using low-cost cameras at the edge and complex 3D world processing is offloaded to the cloud servers. However, cloud-edge MR solutions have distinct challenges such as concurrent multiple camera support, unpredictable compute demands, and CPU/GPU contention. In this paper, we present PointPresence, an edge-compute framework for large-scale MR applications. PointPresence is deployed at an edge node, such as an O-RAN RIC or a local enterprise server, and provides low-latency MR experiences to a large number of users by incorporating intelligent camera selection for compute reduction, reactive pipeline design for adaptation to compute demand changes, and context-aware GPU sharing. Our comprehensive evaluation on an MR testbed shows that PointPresence reduces end-to-end latency by up to 3.5× and improves end user perceived visual quality by 30%.