Journal of Engineering Research

The resource allocation of power 5G slices needs to meet the differentiated requirements of various power services, while traditional allocation methods struggle to cope with dynamically changing network environments and complex service scenarios. The deep reinforcement learning framework can realize the autonomous optimization of resource allocation strategies through continuous interaction between agents and the environment. This framework takes power service requirements and network load status as state inputs, with the goals of maximizing resource utilization and ensuring service QoS. It dynamically adjusts the allocation schemes of computing, storage, bandwidth and other resources by fitting the value function through deep neural networks. It has strong environmental adaptability and decision-making timeliness, which can effectively solve problems such as slice resource competition and load imbalance. It provides technical support for the efficient operation of power 5G networks and is of great significance for promoting the digital transformation of power systems.

Power 5G; Network slicing; Resource allocation; Deep reinforcement learning; Intelligent decision-making