Multi-Agent Quantum Reinforcement Learning for Adaptive Transmission in NOMA-Based Irregular Repetition Slotted ALOHA
This paper proposes a Multi-Agent Quantum Deep Reinforcement Learning (MA-QDRL) framework to optimize uplink access in Irregular Repetition Slotted ALOHA with Non-Orthogonal Multiple Access (IRSA-NOMA) systems under practical constraints such as finite frame lengths and fading channels. IRSA enhance...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11005393/ |
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