AI Based Power Allocation for NOMA

Manglayev T. Kizilirmak R.C. Kho Y.H. Abdul Hamid N.A.W. Tian Y.
June 2022 Springer

Wireless Personal Communications
2022 #124 Issue 4 3253 - 3261 pp.

Novel methods using artificial intelligence for downlink power allocation problem in non-orthogonal multiple access networks are presented. The proposed machine learning and deep learning based methods achieved performance close to the optimum in terms of sum capacity with significantly lower computational costs. The numerical results also demonstrated up to 120 times a boost in computation time as compared to the conventional exhaustive search approach. Furthermore, the training and testing accuracy of the deep learning model reached 0.92 and 0.93 with the loss value dropping up to 0.002.

5G , Artificial intelligence , Deep learning , Machine learning , Non-orthogonal multiple access (NOMA) , Power allocation

Text of the article Перейти на текст статьи

School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Z05H0P9, Kazakhstan
School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand
Laboratory of Computational Sciences and Mathematical Physics, Institute for Mathematical Research, Universiti Putra Malaysia, Selangor, Serdang, 43400, Malaysia
Fujian Key Laboratory of Communication, Network and Information Processing, Xiamen University of Technology, Xiamen, China

School of Engineering and Digital Sciences
School of Engineering and Computer Science
Laboratory of Computational Sciences and Mathematical Physics
Fujian Key Laboratory of Communication

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026