A Collaborative Framework for Edge Caching in Dynamic NGNs: Enhancing Efficiency Through Multi-Agent Learning

Edge caching in Next-Generation Networks (NGNs) deploys cache units in Small-Cell Base Stations (SBSs), enabling User Equipment (UE) to retrieve content locally and allowing neighboring SBSs to share cached data. However, the large-scale deployment of SBSs and the diverse content preferences of UEs create significant challenges for cache strategy design by hindering accurate semantic representation during SBS collaboration, which in turn leads to redundant caching and suboptimal decisions in network environments. To address these issues, we propose a value decomposition-based reward allocation method to optimize network costs. Specifically, we design an agent model capable of explicitly exchanging information to enhance the semantic learning capabilities of SBSs. We then introduce a Neural Attention Additive Q-learning (NA<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Q) model within the Advantage Actor-Critic (A2C) framework, which decomposes joint action values to capture the nonlinear relationships arising from SBS interactions. The model employs a Variational Auto-Encoder (VAE) to construct latent local semantics for each agent and integrates a self-attention mechanism to evaluate each agent’s credit. Finally, a credit-based reward allocation mechanism dynamically assesses SBS contributions, addressing traditional methods’ shortcomings in modeling inter-agent dependencies. Experimental results demonstrate that the proposed method significantly improves cache hit rates while reducing backhaul traffic, outperforming baseline methods.