Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

Abstract

6G targets a broad and ambitious range of networking scenarios with stringent and diverse requirements. Such challenging demands require a multitude of computational and communication resources and means for their efficient and coordinated management in an end-to-end fashion across various domains. Conventional approaches cannot handle the complexity, dynamicity, and end-to-end scope of the problem, and solutions based on artificial intelligence (AI) become necessary. However, current applications of AI to resource management (RM) tasks provide partial ad hoc solutions that largely lack compatibility with notions of native AI enablers, as foreseen in 6G, and either have a narrow focus, without regard for an end-to-end scope, or employ non-scalable representations/learning. This survey article contributes a systematic demonstration that the 6G vision promotes the employment of appropriate distributed machine learning (ML) frameworks that interact through native AI enablers in a composable fashion towards a versatile and effective end-to-end RM framework. We start with an account of 6G challenges that yields three criteria for benchmarking the suitability of candidate ML-powered RM methodologies for 6G, also in connection with an end-to-end scope. We then proceed with a focused survey of appropriate methodologies in light of these criteria. All considered methodologies are classified in accordance with six distinct methodological frameworks, and this approach invites broader insight into the potential and limitations of the more general frameworks, beyond individual methodologies. The landscape is complemented by considering important AI enablers, discussing their functionality and interplay, and exploring their potential for supporting each of the six methodological frameworks. The article culminates with lessons learned, open issues, and directions for future research.