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
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.

Abstract

Abstract