@inproceedings{d00e8c44443f4f4992c5a1f455fa96a7,
title = "Energy-Efficient NOMA Resource Allocation via Deep Transfer Reinforcement Learning",
abstract = "We propose a multi-agent transfer reinforcement learning (RL) framework for energy-efficient resource allocation in a multi-subband multi-user wireless system using nonorthogonal multiple access (NOMA). It instantiates per-subband and per-user agents from pretrained common agents, and adapts them to service-specific network environments.",
keywords = "NOMA, reinforcement learning, resource allocation, transfer learning",
author = "Nguyen, \{Giang Minh\} and Yun, \{Ji Hoon\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Information and Communication Technology Convergence, ICTC 2024 ; Conference date: 16-10-2024 Through 18-10-2024",
year = "2024",
doi = "10.1109/ICTC62082.2024.10827109",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1875--1876",
booktitle = "ICTC 2024 - 15th International Conference on ICT Convergence",
}