Abstract
The spectral efficiency of the visible light communication system can be enhanced by using the non-orthogonal multiple access (NOMA) scheme. In this paper, we propose a deep Q network (DQN) framework-based power allocation scheme that maximizes the sum rate of a NOMA-based cellular VLC network with mobility support. The numerical results indicate that the optimisation process achieves improved performance in terms of sum data rate (SDR) by approximately 8.8, 7, and 4% compared with conventional algorithms, such as fixed power allocation, gain ratio power allocation, and genetic algorithms.
| Original language | English |
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| Title of host publication | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) |
| Publisher | IEEE |
| Pages | 512-517 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350348743 |
| ISBN (Print) | 9798350348750 |
| DOIs | |
| Publication status | Published - 19 Jul 2024 |
| Event | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) - Rome, Italy Duration: 17 Jul 2024 → 19 Jul 2024 Conference number: 14 |
Conference
| Conference | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) |
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| Abbreviated title | CSNDSP |
| Country/Territory | Italy |
| City | Rome |
| Period | 17/07/24 → 19/07/24 |
Keywords
- Deep reinforcement learning
- NOMA
- visible light communication
- power allocation