Foundation Models for Generalizable Semantic and Goal-Oriented Communication

2026-05-24

ICC 2026 - IEEE International Conference on Communications

AI Summary

为解决6G通信中低比特率下泛化能力弱的问题，提出了一种基于基础模型的语义与目标导向通信框架，通过选择性传输关键信息并利用预训练模型重建来提高效率。

Abstract

Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual–linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision–language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87–0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83–0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.

Problem

• Semantic and Goal-Oriented Communication
• Generalization
• Rate Budgets

Innovation

• Foundation Model
• Semantic Communication
• Rate Efficiency
• Sparse Anchors
• Generative Priors