A PPO-based DRL approach with GAT and BiLSTM features, adaptive penalties, and reward shaping optimizes multi-resource allocation for QoS assurance in 5G network slicing.
A survey on beyond 5g network slicing for smart cities applications
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QoS Assurance Mechanism for 5G Network Slicing Based on the Deep Reinforcement Learning PPO Algorithm
A PPO-based DRL approach with GAT and BiLSTM features, adaptive penalties, and reward shaping optimizes multi-resource allocation for QoS assurance in 5G network slicing.