Deep Reinforcement Learning for QoS and Energy Optimization in 5G Network Slicing
Keywords:
5G standalone networks, DRL, Energy efficiency, Network slicing, QoSAbstract
Network slicing is an important feature of fifth-generation (5G) standalone systems, supporting the utilization of heterogeneous services like enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) over a common setup. However, conventional static and heuristic-based slicing methods are unable to effectively adapt to dynamic traffic situations, resulting in suboptimal quality of service (QoS) and energy consumption. To address these problems, this paper suggests an AI-enabled dynamic network slicing structure based on deep reinforcement learning (DRL). A Deep Q-Network (DQN) agent is established to perform real-time resource allocation by discerning network states and optimizing a reward function that jointly reflects QoS parameters and energy efficiency. Simulation results show that the proposed method increases system throughput by up to 25%, decreases URLLC latency to below 10ms, improves packet delivery ratio to 97%, and declines overall energy consumption by approximately 12–17% when compared with conventional approaches. These findings ratify the effectiveness of reinforcement learning for adaptive and energy-efficient network control and highlight its potential to allow autonomous, scalable, and efficient resource orchestration in 5G and upcoming AI-native wireless networks.
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