Deep Reinforcement Learning for Online Resource Allocation in Network Slicing

This article presents a dynamic RAN slicing model and an online PW‑DRL approach that combines deep learning, reinforcement learning, and Lyapunov optimization to allocate resources adaptively, detailing a four‑step decision process, LSTM/CNN predictions, and experimental results showing improved transmission rates and acceptance ratios across DTT, DS, and TO slices.

Lyapunov optimizationNetwork SlicingOFDMA

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Deep Reinforcement Learning for Online Resource Allocation in Network Slicing