Cooperative Content Caching and Delivery in Vehicular Networks:A Deep Neural Network Approach

The growing demand for low delay vehicu-lar content has put tremendous strain on the backbone network.As a promising alternative,cooperative con-tent caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and lim-ited caching capacities.In addition,the high mobil-ity of vehicles renders the more complicated caching environment.Therefore,performing efficient cooper-ative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communica-tion link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is for-mulated.Furthermore,we use a multi-agent deep rein-forcement learning(MADRL)approach to model the decision-making process for caching among heteroge-neous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL ap-proach can enhance hit ratio while reducing transmis-sion delay and cache cost.