Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundari...

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Published inarXiv.org
Main Authors Emilio Calvanese Strinati, Paolo Di Lorenzo, Sciancalepore, Vincenzo, Aijaz, Adnan, Kountouris, Marios, Gündüz, Deniz, Popovski, Petar, Mohamed, Sana, Stavrou, Photios A, Soret, Beatriz, Cordeschi, Nicola, Scardapane, Simone, Merluzzi, Mattia, Zanzi, Lanfranco, Mauro Boldi Renato, Quek, Tony, Nicola di Pietro, ceville, Olivier, Costanzo, Francesca, Li, Peizheng
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.02.2024
Subjects
6G mobile communication
Communication
Data exchange
Distillation
Electromagnetic fields
Intelligence
Intelligent agents
Network latency
Representations
Semantics
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Summary:Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.
ISSN:2331-8422