基于深度强化学习的边缘网络内容协作缓存与传输方案研究OA北大核心CSTPCD

In order to address the problem of rapid increase of data throughput in fifth-generation wireless communication net-works,mobile edge caching has become a useful solution.It can reduce the burden on the backhaul link and core network,cut down service latency by storing network content on edge devices.So far,most edge caching solutions have mainly focused on optimizing cooperative content caching,and ignored the efficiency of content transmission.This paper studied cooperative edge caching and wireless bandwidth allocation problems in ultra-dense networks,calculated the overall similarity between the base stations by using cosine similarity and Gaussian similarity,and grouped the small base stations according to total similarity in the network.The caching and radio bandwidth allocation problems were modeled as a long-term mixed-integer non-linear programming(LT-MINLP).Then,the cooperative edge caching and wireless bandwidth allocation problem were transformed into a constrained Markov decision process.Finally,it proposed cooperative edge caching and radio resource allocation scheme by using the DDPG model.And it proposed deep reinforcement learning based-edge content cooperative caching and bandwidth allocation algorithm CBDDPG.The proposed base station group strategy increased the file sharing opportunity among base sta-tions,the cache scheme of the proposed CBDDPG algorithm used DDPG dual-network mechanism,which could better capture the regularity of user requests and optimize cache deployment.The proposed CBDDPG algorithm was compared to three base-line algorithms,sach as RBDDPG,LCCS and CB-TS in experiments.Experimental results show that the proposed strategy can effectively enhance the content cache hit ratio,reduce the delay of content delivery and improve the user experience.