Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
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arXiv
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| Natura: | Preprint |
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2024
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| author | Strinati, Emilio Calvanese Di Lorenzo, Paolo Sciancalepore, Vincenzo Aijaz, Adnan Kountouris, Marios Gündüz, Deniz Popovski, Petar Sana, Mohamed Stavrou, Photios A. Soret, Beatriz Cordeschi, Nicola Scardapane, Simone Merluzzi, Mattia Zanzi, Lanfranco Renato, Mauro Boldi Quek, Tony di Pietro, Nicola Forceville, Olivier Costanzo, Francesca Li, Peizheng |
| author_facet | Strinati, Emilio Calvanese Di Lorenzo, Paolo Sciancalepore, Vincenzo Aijaz, Adnan Kountouris, Marios Gündüz, Deniz Popovski, Petar Sana, Mohamed Stavrou, Photios A. Soret, Beatriz Cordeschi, Nicola Scardapane, Simone Merluzzi, Mattia Zanzi, Lanfranco Renato, Mauro Boldi Quek, Tony di Pietro, Nicola Forceville, Olivier Costanzo, Francesca Li, Peizheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach Strinati, Emilio Calvanese Di Lorenzo, Paolo Sciancalepore, Vincenzo Aijaz, Adnan Kountouris, Marios Gündüz, Deniz Popovski, Petar Sana, Mohamed Stavrou, Photios A. Soret, Beatriz Cordeschi, Nicola Scardapane, Simone Merluzzi, Mattia Zanzi, Lanfranco Renato, Mauro Boldi Quek, Tony di Pietro, Nicola Forceville, Olivier Costanzo, Francesca Li, Peizheng Signal Processing 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. |
| title | Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2402.07573 |