Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach
The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870268/ |
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