Abstract
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.
Original language | English |
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Pages (from-to) | 3253-3261 |
Number of pages | 9 |
Journal | Wireless Personal Communications |
Volume | 124 |
Issue number | 4 |
DOIs | |
Publication status | Published - Jun 2022 |
Keywords
- 5G
- Artificial intelligence
- Deep learning
- Machine learning
- Non-orthogonal multiple access (NOMA)
- Power allocation
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering