Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission.

翻译：大模型已成为推动未来网络智能应用普及的关键赋能技术。然而，智能应用带来的数据流量激增对未来网络的资源利用和能耗造成压力。语义通信凭借其高效的内容理解能力，在减少智能应用数据传输方面展现出巨大潜力。本文研究语义感知网络中由大模型驱动的资源分配问题。具体而言，设计了一种基于场景图模型与多模态预训练模型的语义感知通信网络架构，以实现高效数据传输。在所提出的网络架构基础上，进一步提出一种语义感知网络中的智能资源分配方案，以提升资源利用效率。在该资源分配方案中，采用语义传输质量作为评估指标，并分析了无线信道衰落对语义传输的影响。为最大化多用户的语义传输质量，设计了一种基于扩散模型的决策方案来解决语义感知网络中的功率分配问题。仿真结果表明，所提出的大模型驱动网络架构与资源分配方案能够实现高质量的语义传输。