Multi-LLM Cooperation-Based Joint Intent-Driven Network Slicing and Resource Allocation

The exponential growth of heterogeneous mobile applications has raised challenges in current Radio Access Networks (RAN). While the network slicing technique offers virtualized networks with independent resources to satisfy diverse Quality of Service (QoS) requirements, persistent challenges remain in dynamic resource allocation and intent-driven slice configuration. For resource allocation, conventional Deep Reinforcement Learning (DRL)-based approaches encounter challenges in addressing the high-dimensional state-action space in large-scale networks with massive Base Stations (BS) and users. For slice configuration, current static intent-driven slice management frameworks suffer from inflexible architectures that fail to adaptively optimize network slicing configurations. In this paper, we propose a multi-Large Language Model (LLM) Cooperation-based Joint Network Slicing and Resource Allocation (JNSRA) algorithm to jointly optimize the configurations of intent-driven network slices and resource allocation in large-scale multi-BS networks. In JNSRA, we propose to train Agent LLM through LLM alignment, which can enhance cooperation among slices and BS by regarding joint resource allocation actions as sequences. Moreover, we propose a multi-LLM joint optimization framework to jointly optimize Agent LLM and Intent LLM, leading to enhanced slicing and allocation capabilities. Simulations illustrate that JNSRA outperforms other DRL and heuristic approaches, and the proposed multi-LLM collaboration can further enhance the reward, satisfaction ratio and throughput.