Optimizing MDS-coded cache-enable wireless network:a blockchain-based cooperative deep reinforcement learning approach

Mobile distributed caching(MDC)as an emerging technology has drawn attentions for its ability to shorten the distance between users and data in the wireless network.However,the DC network state in the existing work is always assumed to be either static or real-time updated.To be more realistic,a periodically updated wireless network using maximum distance separable(MDS)-coded DC is studied,in each period of which the devices may arrive and leave.For the efficient optimization of the system with large scale,this work proposes a blockchain-based cooperative deep reinforcement learning(DRL)approach,which enhances the efficiency of learning by cooperating and guarantees the security in cooperation by the practical Byzantine fault tolerance(PBFT)-based blockchain mechanism.Numerical results are presented,and it illustrates that the proposed scheme can dramatically reduce the total file download delay in DC network under the guarantee of security and efficiency.