Network slicing resource management distributed architecture for multi-base station demand awareness
A multi-base station demand-aware network slicing architecture based on Distributed Multi-Intelligent Robust Proximal Policy Optimization(DMRPPO)is proposed to address the problems of slow user demand sensing and high computational and storage pressure caused by diverse service demands and the increase in the number of base stations in B5G network slicing.This architecture consists of a parameter server,a sample cache,and multiple Robust Proximal Policy Optimization(RPPO)agent.In which each base station has an RPPO and a sample cache,the former introduces dominant value normalization,policy entropy and value trimming optimization mechanisms based on the PPO to explore efficient resource management schemes,and the latter stores user sample data.The parameter server is responsible for extracting data from the sample cache to update the parameters and publish them to the RPPO,and the agent in the base station uses the published parameters to realize the resource allocation of each base station.Simulation results show that the proposed improves spectral efficiency,service level agreement satisfaction rate,system utility and user demand awareness speed.
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