Modeling of L-Band Erbium-Doped Fiber Amplifiers Using Artificial Neural Networks
This work presents the modeling of L-band EDFAs using artificial neural networks, specifically comparing the performance between Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN) architectures. Experimental data encompassing various amplification scenarios were used to train the models under different input configurations. The analysis demonstrates that the RNN model outperforms MLP in terms of convergence and estimation accuracy for the chosen amplifier figures of merit. Overall, a high-precision modeling is achieved, enabling the simulation and optimization of wideband amplification schemes under flexible transmission conditions with low complexity and low computational cost.
GAM Antenna Arrays for B5G Systems
This paper investigates and compares irregular antenna arrays based on Golden Angle Modulation (GAM) with conventional Uniform Rectangular Arrays (URA) for 5G millimeter-wave (mmWave) applications, with a focus on mitigating grating lobes. A half-wave dipole antenna operating at 26 GHz is designed and integrated into various array configurations: URAs (0.5λ, 5λ) and GAM arrays (1.45λ, 2.91λ). Simulations are performed to evaluate key performance metrics, including peak gain, half-power beamwidth (HPBW), and sidelobe level (SLL). Results show that while URAs with large spacing suffer from severe grating lobes (e.g., SLL of -0.29 dB for 5λ spacing), while the aperiodic structure of GAM arrays effectively suppresses them. The GAM 1.45λ array with 1.45λ spacing achieves a high peak gain of 22.16 dBi, a narrow HPBW of 2.5°, and an improved SLL of -16.71 dB. Beamforming analysis for this GAM array demonstrates robust steering capabilities for moderate angles (e.g., 20.50 dBi at 30°), with a typical gradual gain degradation at wider angles (e.g., 8.91 dBi at 75°). This work highlights the superior performance of GAM arrays in mitigating grating lobes and providing effective beam steering, making them a promising solution for future wireless communication systems.
Photonic Spatial Switching Techniques Using Semiconductor Optical Amplifiers
This work presents a detailed analysis of the key switching parameters of semiconductor optical amplifiers (SOAs). Based on a numerical model of an InPhenix SOA, a dataset was generated, enabling a rigorous examination of the principal parameters of the PISIC and MISIC techniques. Furthermore, a Multi-Layer Perceptron (MLP) was employed to solve the inverse SOA problem, achieving a low mean squared error.
Optimizing L-Band EDFA Pumping Schemes via Recurrent Neural Networks and Water Cycle Algorithm
This work presents an analysis of L-band Erbium-Doped Fiber Amplifier (EDFA) performance under various pumping configurations. We employed an artificial neural network and a multi-objective optimization algorithm to find the amplifier Pareto front for gain and noise figure. To achieve this, we utilized a recurrent neural network (RNN), trained with experimentally acquired data, jointly with the Multi-Objective Water Cycle Algorithm (MOWCA). Results demonstrate the feasibility of achieving reasonable gains (up to 22 dB) with low noise figures (around 4 dB) across various pumping configurations. Furthermore, we observed a potential gain increment of up to 3.5 dB without significant noise figure degradation when employing bi-directional pumping compared with forward pumping. These insights may contribute to the design of more efficient L-band EDFAs.
Delayed Luminescence of Algae Under Silver Stress
The stress of algae samples after exposure to Silver Nitrate (AgNO3) is expressed in alterations of the decay dynamics of their delayed luminescence patterns. Indicative results are obtained just one hour after inoculation, with sensitivity comparable to those of standard tests (obtained after 72h) but using less consumables/work-hours. Further tests using different metals might validate the new data analysis suggested in the article.
Integrated Photonic Enabling Massive Data Transfer: A Short Review
We have been living in a digital revolution, with the creation of new online services, the popularization of electronics, the advent of generative AI, and a growing trend of virtualization of things, allowing the creation of a digital twin version of a real object. As this revolution pushes society forward, there is a high performance demand for its infrastructure, such as telecommunication networks. There is a constant need to transmit a massive amount of data, which is increasing with the market demand. The scientific community has been working to bring new solutions to this demand. One possibility is simplifying the edge devices, adding open issues, and reducing system latency. In this context, photonic integrated circuits can play a significant role. This work concisely reviews the current development of photonics integrated circuits (PIC) for optical systems with strict requirements and the demand for massive data transfer. As the current telecommunication scenario rapidly evolves due to market demands and the creation of new requirement-sensitive services, it is crucial to understand the role of integrated photonics in telecommunication networks as discussed in this work. Also, it is presented how open issues, such as chromatic dispersion, can be handled by PIC, leveraging the overall performance of optical telecommunication systems.
Experimental Crosstalk Analysis in SDM Systems with Multimode Fiber Geometry Mismatch
The crosstalk performance of different 50μ m GIMMFs with 45 spatial modes is studied using a packaged multi-plane light converter and a 96×96 optical switch for full cardinality characterization. The impact of combining different refractive index geometries and of environment-induced perturbations are addressed.
Fiber Sensing in the 6G Era: Vision Transformers for ϕ-OTDR-Based Road-Traffic Monitoring
This article adds to the emergent body of research that examines the potential of 6G as a platform that can combine wired and wireless sensing modalities. We apply vision transformers (ViTs) in a distributed fiber-optic sensing system to evaluate road traffic parameters in smart cities. Convolutional neural networks (CNNs) are also assessed for benchmarking. The experimental setup is based on a direct-detection phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) implemented using a narrow linewidth source. The monitored fibers are buried on the university campus, creating a smart city environment. Backscattered traces are consolidated into space–time matrices, illustrating traffic patterns and enabling analysis through image processing algorithms. The ground truth is established by traffic parameters obtained by processing video camera images monitoring the same street using the YOLOv8 model. The results indicate that ViTs outperform CNNs for estimating the number of vehicles and the mean vehicle speed. While a ViT necessitates a significantly larger number of parameters, its complexity is similar to that of a CNN when considering multiply–accumulate operations and random access memory usage. The processed dataset has been made publicly available for benchmarking.