标题： 边缘大型人工智能模型：革命性6G网络 显示英文标题

标题： Edge Large AI Models: Revolutionizing 6G Networks

摘要： 大型人工智能模型（LAMs）具备类似人类的能力，能够解决各种现实世界中的问题，展示了各领域和模态专家的潜力。通过利用地理分布的边缘设备的通信和计算能力，边缘LAM成为了一项使能技术，能够支持在6G中提供各种实时智能服务。 与主要使用小型模型支持单一任务的传统边缘人工智能（AI）不同，边缘LAM的特点在于需要对大型模型进行分解和分布式部署，并且能够支持高度泛化和多样化的任务。 然而，由于无线网络中的通信、计算和存储资源有限，大量的可训练神经元和显著的通信开销构成了边缘LAM实际部署的巨大障碍。 本文从模型分解和资源配置的角度探讨了边缘LAM的机会和挑战。具体而言，我们提出了协作微调和全参数训练框架，以及辅助推理架构，以增强边缘LAM在无线网络中的部署。 此外，我们研究了边缘LAM在空口设计中的应用，重点关注信道预测和波束成形。 这些创新的框架和应用为推进6G技术提供了有价值的见解和解决方案。 显示英文摘要

摘要： Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and modalities. By leveraging the communication and computation capabilities of geographically dispersed edge devices, edge LAM emerges as an enabling technology to empower the delivery of various real-time intelligent services in 6G. Unlike traditional edge artificial intelligence (AI) that primarily supports a single task using small models, edge LAM is featured by the need of the decomposition and distributed deployment of large models, and the ability to support highly generalized and diverse tasks. However, due to limited communication, computation, and storage resources over wireless networks, the vast number of trainable neurons and the substantial communication overhead pose a formidable hurdle to the practical deployment of edge LAMs. In this paper, we investigate the opportunities and challenges of edge LAMs from the perspectives of model decomposition and resource management. Specifically, we propose collaborative fine-tuning and full-parameter training frameworks, alongside a microservice-assisted inference architecture, to enhance the deployment of edge LAM over wireless networks. Additionally, we investigate the application of edge LAM in air-interface designs, focusing on channel prediction and beamforming. These innovative frameworks and applications offer valuable insights and solutions for advancing 6G technology.