Citation

Ahmad, Shabeer and Zhang, Jinling and Khan, Adil and Hayat, Babar and Ullah, Yasir and Khan, Wali Ullah and Ajaib Khan, Umar (2026) Deep learning-assisted resource allocation and channel modeling for UAV to ground communication in ultra-wideband environments. Journal of King Saud University Computer and Information Sciences, 38 (5). ISSN 1319-1578

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Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed in ultra-wideband (UWB) environments to support applications that demand high data rates and reliable connectivity. However, effective communication between UAVs and ground terminals is challenged by six-dimensional (6D) posture-induced signal variation, non-stationary channel behavior, and dynamic resource constraints. Existing models are limited in addressing 6D posture-aware fading, frequency non-stationarity, and spatiotemporal variability in multipath propagation, which are critical in UAV-to-ground (U2G) communication in UWB environments. This study introduces a 6D UAV posture based non-stationary U2G channel model that incorporates UWB signal features, 6D UAV posture dynamics, and intelligent reflecting surface (IRS) beam control. A graph neural network (GNN) is developed to learn spatiotemporal correlations among UAV 6D movement, IRS states, and frequency-selective channel behavior. The model is initialized with ray-tracing (RT) data and supports resource allocation (RA) by adjusting UAV transmit power and IRS reflection coefficients. Performance is evaluated using key statistical metrics, including space–time–frequency correlation functions (STF-CFs), power delay profiles (PDP), root-mean-square delay spread (RMS-DS), and Doppler spectral density (DPSD). Results show that the proposed framework effectively captures 6D posture-aware fading and frequency non-stationarity across wide bandwidths, achieving an 11.6% reduction in path-loss RMSE, 8.2% improvement in RMS-DS fidelity, and 23.4% increase in sum-rate compared with conventional baselines.

Item Type:	Article
Uncontrolled Keywords:	UAV to ground communication, Ultra-wideband environments, Intelligent reflecting surface, Resource allocation, Channel modeling,6D posture dynamics, Deep learning
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Aug 2026 01:29
Last Modified:	04 Aug 2026 01:29
URII:	http://shdl.mmu.edu.my/id/eprint/16447

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