Mobility-Aware Edge Caching with Transformer-DQN in D2D-Enabled Heterogeneous Networks

In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction. Then, within a three-tier heterogeneous network, we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network (DQN) caching strategy. This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations (SBSs) and mobile users (MUs) to reduce end-to-end delay in multi-hop content retrieval. Simulation results show that the proposed DQN-based method outperforms other baseline strategies across various metrics, achieving a 17.2% reduction in transmission delay compared to DQN methods without mobility integration, thus validating the effectiveness of the joint optimization of location prediction and caching decisions.