MACC: A Heterogeneous Multi-Agent Mobile Edge Coding Caching Strategy for Reducing Traffic Load
Xuemei Yuan, Ningjiang Chen · 2023
The rapid development of industrial IoT communication and the wide application of smart terminal devices, large-scale mobile data traffic and frequent data requests pose challenges to resource-constrained wireless communication networks. In order to reduce the network load, the introduction of content encoding caching to the network edge is considered an effective solution. Due to the heterogeneity of the resources of the mobile edge system, the variability of user requests, and the mobility of users, the coded caching strategy at the mobile edge needs to be continuously optimized. However, existing articles do not adequately investigate how to intelligently update the coded caching policy in dynamic environments. For this reason, this paper proposes a heterogeneous multi-agent MDS coded caching scheme (MACC) based on value decomposition, which considers the edge caching server with storage capacity and the mobile user as two different types of agents and improves the caching policy through interaction. In addition, considering the heterogeneous preference of heterogeneous multi-agent and the fact that an increase in the number may lead to a sharp increase in the training dimension, the idea of a value decomposition network is introduced in the training learning of the coded caching scheme. Simulation experiments verify that the proposed MACC can effectively reduce the load on the forward link and achieve a higher cache hit rate.