Citation

Ahmed, Naeem and Ali, Farman and Ahmad, Wasim and Ullah, Yasir and Roslee, Mardeni and Ahmad, Shabeer (2025) Path Loss Prediction Analysis for UAV to Maritime Channel Model using Meta Learning. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

Unmanned aerial vehicles (UAVs) have become increasingly essential in contemporary communication systems, necessitating precise channel models for UAV-to-maritime (U2M) interactions, which have remained challenging due to dynamic environmental conditions. This research has presented a hybrid machine learning framework combining meta-learning and long short-term memory (LSTM) networks to predict path loss (PL) for U2M non-line-of-sight (NLoS) scenarios. The proposed model incorporates critical environmental variables, including atmospheric attenuation, rain attenuation, and sea state dynamics, alongside operational parameters such as UAV altitude, antenna positioning, and propagation amplitude. By integrating meta-learning with LSTM, our framework captured temporal interdependencies and adapted to evolving channel conditions, enhancing robustness in dynamic maritime environments. The time-variant statistical properties, such as cumulative distribution function (CDF) and PL fluctuations, are analyzed and analytically derived to refine fading statistics, delay spread, and predictive accuracy. The results of the proposed framework are compared with theoretical results and existing models, such as the International Telecommunication Union (ITU) and the 3rd Generation Partnership Project (3GPP). Finally, the outcomes of the proposed model show that the simulation results align substantially with the corresponding theoretical results.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	U2M, path loss, atmospheric attenuation, sea state, LSTM, meta-learning
Subjects:	T Technology > TC Hydraulic engineering. Ocean engineering
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	19 Mar 2026 02:23
Last Modified:	19 Mar 2026 02:23
URII:	http://shdl.mmu.edu.my/id/eprint/15617

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