Abstract
We studied the characteristics, regions of existence, and stability of different types of solitons for a distributed model of a mode-locked laser whose dispersion is purely quartic and normal. Among the different types of solitons, we identified three main branches that are named according to their different amplitude: low, medium, and high amplitude solitons. It was found that the first solitons are always unstable while the latter two exist and are stable in relatively large regions of the parameter space. Moreover, the stability regions of medium and high amplitude solitons overlap over a certain range of parameters, manifesting effects of bistability. The energy of high amplitude solitons increases quadratically with their width, whereas the energy of medium amplitude solitons may decrease or increase with the width depending on the parameter region. Furthermore, we have investigated the long term evolution of the continuous-wave solutions under modulational instability, showing that medium amplitude solitons can arise in this scenario. Additionally, we assessed the effects of second- and third-order dispersion on medium and high amplitude solitons and found that both remain stable in the presence of these terms.

Abstract
Optical Wireless Communications (OWC) has recently emerged as a viable alternative to radio-frequency technology, especially for the Internet of Things (IoT) domain. However, current simulation tools primarily focus on physical layer modelling, ignoring network-level issues and energy-constrained environments. This paper presents an energy-aware OWC module for ns-3 that addresses these limitations. The module includes specific PHY and MAC layers and integrates an energy model, a mobility model, and models of monochromatic transceivers and photodetectors, supporting both visible light and infrared (IR) communications. Verification against MATLAB simulations confirms the accuracy of our implementation. Additionally, mobility tests demonstrate that an energy-restricted end device transmitting via IR can maintain a stable connection with a gateway at distances up to 2.5 m, provided the SNR is above 10 dB. These results confirm the capabilities of our module and its potential to facilitate the development of energy-efficient OWC-based IoT systems. © 2025 IEEE.

Abstract

Abstract

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-Time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges 5.2× faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms. © 2025 Elsevier B.V., All rights reserved.

Abstract
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5 GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%. © 2025 IEEE.