DQN-driven framework for multi-slice 6G resource allocation and edge caching that outperforms traditional methods on latency and throughput in simulations.
Federated deep reinforcement learning for open ran slicing in 6g networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
DQN-driven framework for multi-slice 6G resource allocation and edge caching that outperforms traditional methods on latency and throughput in simulations.