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

2025-01-23

AI Summary

To address the high resource consumption and difficulty in guaranteeing semantic transmission quality in semantic communications, this paper proposes a semantic-aware intelligent resource allocation architecture. Methodologically, it integrates scene graphs with multimodal large language models (LLMs) to construct a unified semantic representation and transmission framework; for the first time, deeply embeds LLMs into the resource allocation pipeline, optimizing explicitly for semantic fidelity; jointly models the impact of channel fading on semantic distortion and designs a diffusion-model-based power allocation mechanism. The key contributions are: (i) breaking away from conventional bit-level optimization paradigms toward end-to-end, semantic-driven resource coordination; and (ii) achieving significant improvements in multi-user semantic fidelity, while reducing redundant data volume and system energy consumption—thereby paving a novel pathway toward green semantic communications. Simulation results validate substantial gains in both semantic accuracy and energy efficiency.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Semantic Communication
• Resource Allocation
• Energy Efficiency

Innovation

• Semantic Communication
• Diffusion Model
• Multi-modal Pre-training