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Openness and intelligence are two enabling features to be introduced in next generation wireless networks, for example, Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.
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Decentralized multi-party multi-network AI for global deployment of 6G wireless systems
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiberoptical domain. 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. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.
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- Award ID(s):
- 2029295
- PAR ID:
- 10587670
- Publisher / Repository:
- arXiv:2407.01544 [cs.NI], Apr. 2024
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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