标题： 基于大模型的未来语义感知网络中的资源分配 显示英文标题

标题： Resource Allocation Driven by Large Models in Future Semantic-Aware Networks

摘要： 大模型已成为未来网络智能应用普及的关键推动者。 然而，智能应用带来的数据流量激增给未来网络的资源利用和能耗带来了压力。 凭借高效的内容理解能力，语义通信在减少智能应用中的数据传输方面具有显著潜力。 本文研究了语义感知网络中由大模型驱动的资源分配。 具体来说，设计了一种基于场景图模型和多模态预训练模型的语义感知通信网络架构，以实现高效的数据传输。 在所提出的网络架构基础上，提出了一种语义感知网络中的智能资源分配方案，以进一步提高资源利用效率。 在资源分配方案中，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。 为了最大化多个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，以解决语义感知网络中的功率分配问题。 仿真结果表明，所提出的基于大模型的网络架构和资源分配方案实现了高质量的语义传输。 显示英文摘要

摘要： 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.