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.
Zhao-Kun Shao, Kangyu Gao, Zhi-hui Zhan et al.· International Conference on...· 0 citations
Efficient long-term network evolution is becoming increasingly critical in dense 5G-Advanced and beyond cellular systems, where persistent traffic imbalances and localized congestion pose significant challenges that conventional short-term radio resource management alone cannot fully mitigate. This paper proposes a digital twin (DT)-enabled non-real-time (NRT) network evolution framework integrated with a large language model (LLM). Within this architecture, the digital twin provides a high-fidelity, controllable environment for evaluating infrastructure actions, while the LLM serves as a strategic orchestration engine that recommends cost-efficient network upgrades based on observed network states. Unlike traditional optimization methods that require exhaustive mathematical reformulations for each specific scenario, the proposed framework leverages the reasoning capabilities of LLMs to interpret operator objectives and constraints in natural language, generating structured evolution plans. The considered NRT action space encompasses antenna upgrades, bandwidth expansion, and new base station (BS) deployment. A techno-economic formulation is introduced to jointly evaluate load reduction performance and overall economic expenditure. Numerical results in a dense cellular scenario demonstrate that the framework effectively reduces peak resource utilization and provides diverse, coordinated evolution strategies tailored to varying network conditions.
Yukai Wang, Janghee Woo, G. Hahm et al.· International Conference on...· 0 citations