Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore,, Adnan Aijaz, Marios Kountouris, Deniz G\"und\"uz, Petar Popovski, Mohamed, Sana, Photios A. Stavrou, Beatriz Soret, Nicola Cordeschi, Simone Scardapane,, Mattia Merluzzi, Lanfranco Zanzi, Mauro Boldi Renato

TL;DR
This paper introduces the 6G-GOALS approach, integrating goal-oriented and semantic communication into AI-native 6G networks to improve information exchange efficiency and support AI federation.
Contribution
It presents a novel framework combining semantic, pragmatic, and goal-oriented communication principles tailored for AI-native 6G networks, with proof-of-concept implementations.
Findings
Enhanced data relevance and efficiency in communication
Reduction in bandwidth, latency, and energy consumption
Feasibility demonstrated through proof-of-concept use cases
Abstract
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,…
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Taxonomy
TopicsRobotics and Automated Systems · IoT and Edge/Fog Computing
MethodsFocus
