Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration

• URL: http://arxiv.org/abs/2310.17471v1
• Date: Thu, 26 Oct 2023 15:19:40 GMT
• Title: Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration
• Authors: Xiang Chen, Zhiheng Guo, Xijun Wang, Howard H. Yang, Chenyuan Feng, Junshen Su, Sihui Zheng, Tony Q. S. Quek
• Abstract summary: We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
• Score: 56.330705072736166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.

Related papers

• Generative AI Enabled Matching for 6G Multiple Access [51.00960374545361]
  We propose a GenAI-enabled matching generation framework to support 6G multiple access. We show that our framework can generate more effective matching strategies based on given conditions and predefined rewards.
  arXiv  Detail & Related papers  (2024-10-29T13:01:26Z)
• Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems [31.754166695074353]
  This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks.
  arXiv  Detail & Related papers  (2024-04-15T15:21:25Z)
• AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems [3.5370806221677245]
  This paper explores the seamless integration of Large Language Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems. LLMs and GPTs will collaboratively take center stage alongside traditional pre-generative AI and machine learning (ML) algorithms.
  arXiv  Detail & Related papers  (2023-11-10T02:59:16Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks [61.416494781759326]
  In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level. We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
  arXiv  Detail & Related papers  (2022-10-07T13:41:15Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• Federated Learning for 6G Communications: Challenges, Methods, and Future Directions [71.31783903289273]
  We introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.
  arXiv  Detail & Related papers  (2020-06-04T15:17:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.