A System-Level Simulation Module for Multi-UAV IRS-Assisted Communications

Abstract

Sixth-Generation (6G) networks are set to provide reliable, widespread, and ultra-low-latency mobile broadband communications for a variety of industries. In this regard, the Internet of Drones (IoD) represents a key component for the development of 3D networks, which envisions the integration of terrestrial and non-terrestrial infrastructures. The recent employment of Intelligent Reflective Surfaces (IRSs) in combination with Unmanned Aerial Vehicles (UAVs) introduces more degrees of freedom to achieve a flexible and prompt mobile coverage. As the concept of smart radio environment is gaining momentum across the scientific community, this work proposes an extension module for Internet of Drones Simulator (IoD-Sim), a comprehensive simulation platform for the IoD, based on Network Simulator 3 (ns-3). This module is purposefully designed to assess the performance of UAV-aided IRS-assisted communication systems. Starting from the mathematical formulation of the radio channel, the simulator implements the IRS as a peripheral that can be attached to a drone. Such device can be dynamically configured to organize the IRS into patches and assign them to assist the communication between two nodes. Furthermore, the extension relies on the configuration facilities of IoD-Sim, which greatly eases design and coding of scenarios in JavaScript Object Notation (JSON) language. A simulation campaign is conducted to demonstrate the effectiveness of the proposal by discussing several Key Performance Indicators (KPIs), such as Radio Environment Map (REM), Signal-to-Interference-plus-Noise Ratio (SINR), maximum achievable rate, and average throughput.

Cite this work

@article{grieco2024systemlevel,
  title = {{A System-Level Simulation Module for Multi-UAV IRS-Assisted Communications}},
  author = {Giovanni Grieco and Giovanni Iacovelli and Daniele Pugliese and Domenico Striccoli and Luigi Alfredo Grieco},
  journal = {IEEE Transactions on Vehicular Technology, vol. 73, no. 5},
  year = {2024},
  doi = {10.1109/tvt.2023.3342298},
  url = {https://doi.org/10.1109/tvt.2023.3342298}
}

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