Coded Caching Optimization in Dual Time-Scale Wireless Networks: An Advantage Actor–Critic Learning Approach

The rapid growth of mobile data traffic poses significant challenges to ensuring high-quality service in wireless networks. Although the caching technique is capable of alleviating network congestion, most existing schemes depend on uncoded caching with prior knowledge of content popularity and ignore the time-scale mismatch between content dynamics and user mobility. To address these challenges, we first formulate a dynamic coded caching optimization framework under a dual time-scale model that simultaneously captures long-term content popularity evolution and short-term user mobility patterns. Then, we model the optimization problem as a Markov decision process and design a novel advantage actor–critic (A2C) based coded caching algorithm. By introducing the advantage function, the proposed approach can mitigate variance in policy updates and accelerate convergence under the caching capacity constraint. Finally, extensive simulations are conducted to demonstrate that our proposed algorithm significantly outperforms baseline caching schemes in terms of average delay cost.