A Hierarchical Multi-RAT MEC Framework with LLM-based EC Method

Abstract

To support heterogeneous services in 5G and beyond, multi-radio access technology (multi-RAT) mobile edge computing (MEC) systems must jointly handle user association and radio resource allocation under stringent delay requirements. This problem becomes particularly challenging when ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) users coexist, since the former requires strict deadline satisfaction while the latter emphasizes low average latency under computation-intensive workloads. In this paper, we propose a hierarchical multi-RAT MEC framework with large language model (LLM)-assisted evolutionary computation (EC). The outer layer uses an LLM to generate user–base station association decisions, while the inner layer employs EC for bandwidth allocation under the given association pattern. This design combines the global reasoning capability of LLMs with the continuous optimization strength of EC. Simulation results show that the proposed LLM-hRAT scheme converges faster and achieves lower average cost than conventional single-layer EC methods and heuristic non-LLM baselines in both uniform and non-uniform user deployment scenarios.