Adaptive Multi-Layered Non-Terrestrial Network for Deep Learning-Enhanced Global Connectivity
Year: 2024
Type: conference-paper
Abstract: This paper presents the Adaptive Deep Learning-Enhanced Non-Terrestrial Network (ADL-NTN), an innovative framework that combines satellites, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). By integrating Free-Space Op- tical (FSO) and Radio Frequency (RF) communications optimised for different altitudes, this architecture aims to improve connectivity in remote and disaster-affected regions. The ADL-NTN employs deep learning algorithms for dynamic power distribution and link opti- misation, significantly enhancing the network’s robustness and adaptability to environmen- tal conditions and varying demands. Simulations conducted in OMNeT++ demonstrate substantial improvements, with throughput increasing by up to 37% and latency decreas- ing by 42%, surpassing traditional NTN systems. The ADL-NTN architecture exhibits exceptional resilience, ensuring high-quality service delivery under diverse conditions. This research sets the stage for integrating future communication technologies and expanding the framework for global implementation. The ADL-NTN offers groundbreaking solutions for enhancing rural connectivity and providing rapid disaster response, significantly con- tributing to global digital inclusion
Source: Kalpa publications in computing
DOI: 10.29007/l5lc
Authors: Osei Wusu Brempong Jnr, Junaid Hussain Muzamal, Okechukwu Clement Agomuo, Zohaib Khan
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403508017