Citation

Shanmugavel, Anantha Babu and Paduvilan, Arjun Kidavunil and Mariappan, E and Sivakumar, Nithya Rekha and Alabdultif, Abdulatif and Sayeed, Md Shohel (2026) A Multi Agent Hybrid Transfer Learning and Monte Carlo Tree Search Framework for Energy Efficient UAV Communication in 6G Aerial Networks. IEEE Open Journal of the Communications Society. p. 1. ISSN 2644-125X

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Abstract

Maintaining communication between Unmanned Aerial Vehicles (UAVs) and Ground Control Stations (GCS) over an extended set of operational distances is still a major challenge in autonomous aerial systems, even within new 6G-enabled aerial network models. The traditional measures of communication management, such as heuristic rule-based approaches and classical deep learning approaches, tend to be inadequate to change the dynamic channel conditions and put computational pressures on resourceconstrained UAV platforms. To improve the efficiency of multi-agent UAV communication, this paper will suggest a lightweight hybrid optimization framework that combines transfer learning and a pre-trained deep neural network with a Monte Carlo Tree Search (MCTS)-based decision-making component. The neural network element is used to predict the quality of communication link and the cost of transmitting energy based on real-time signal characteristics of Received Signal Strength Indicator (RSSI), distance of communication, and the level of interference and left over battery power. The MCTS module uses these semantic predictions to dynamically find the best communication strategies, such as direct GCS transmission, relay-assisted routing, and adaptive data-rate control in response to the changing conditions of channels and range. The proposed architecture presented is decentralized, compared to traditional DRL based communication frameworks involving expensive end-to-end policy learning, the proposed TL-MCTS framework divides the lightweight semantic communication estimation from the decentralized search-based optimization to mitigate the computational burden and enhance the adaptability in real-time for large-scale UAV communication environments. Moreover, the framework was tested in dense UAV swarm environment with 10 to 100 UAV agents with repeated experiments and evaluated the communication performance through the help of confidence interval analysis and variance calculation, which validated the communication performance statistically in the dynamic wireless environment.

Item Type:	Article
Uncontrolled Keywords:	Unmanned Aerial Vehicles (UAVs), Transfer Learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 06:08
Last Modified:	02 Sep 2026 06:08
URII:	http://shdl.mmu.edu.my/id/eprint/16526

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