空天地一体化网络中基于联邦深度强化学习的边缘协作缓存策略OA北大核心

To address the problem of limited network coverage in remote areas,combining space-air-ground integrated network with mobile edge computing could provide low-latency and high-reliability transmissions for user requests in these areas,as well as timely caching services.Considering the dynamic change of the topology of the space-air-ground integrated network and the content popularity being constantly updated,a network architecture of space-air-ground inte-grated edge collaborative caching was proposed first.Then,the cache replacement problem for edge servers was mod-eled as a Markov decision process.Finally,a federated discrete soft actor-critic(FDSAC)algorithm was proposed,with the core idea of integrating a weighted attention mechanism into the federated learning framework and incorporating a bi-directional long short-term memory network into the DSAC model.With the reconfigured reward function as the optimi-zation objective,the optimal cache replacement policy was learned by maximizing the expectation of negative long-term rewards.Simulation results show that compared with other algorithm,the proposed algorithm can improve the cache hit rate of user requests by 18%and reduce the access latency of content by 25%while protecting user privacy.