Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions
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arXiv
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| Format: | Preprint |
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2024
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| author | Shokrnezhad, Masoud Mazandarani, Hamidreza Taleb, Tarik Song, Jaeseung Li, Richard |
| author_facet | Shokrnezhad, Masoud Mazandarani, Hamidreza Taleb, Tarik Song, Jaeseung Li, Richard |
| contents | In the context of emerging 6G services, the realization of everything-to-everything interactions involving a myriad of physical and digital entities presents a crucial challenge. This challenge is exacerbated by resource scarcity in communication infrastructures, necessitating innovative solutions for effective service implementation. Exploring the potential of Semantic Communications (SemCom) to enhance point-to-point physical layer efficiency shows great promise in addressing this challenge. However, achieving efficient SemCom requires overcoming the significant hurdle of knowledge sharing between semantic decoders and encoders, particularly in the dynamic and non-stationary environment with stringent end-to-end quality requirements. To bridge this gap in existing literature, this paper introduces the Knowledge Base Management And Orchestration (KB-MANO) framework. Rooted in the concepts of Computing-Network Convergence (CNC) and lifelong learning, KB-MANO is crafted for the allocation of network and computing resources dedicated to updating and redistributing KBs across the system. The primary objective is to minimize the impact of knowledge management activities on actual service provisioning. A proof-of-concept is proposed to showcase the integration of KB-MANO with resource allocation in radio access networks. Finally, the paper offers insights into future research directions, emphasizing the transformative potential of semantic-oriented communication systems in the realm of 6G technology. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_00081 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions Shokrnezhad, Masoud Mazandarani, Hamidreza Taleb, Tarik Song, Jaeseung Li, Richard Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Machine Learning Networking and Internet Architecture In the context of emerging 6G services, the realization of everything-to-everything interactions involving a myriad of physical and digital entities presents a crucial challenge. This challenge is exacerbated by resource scarcity in communication infrastructures, necessitating innovative solutions for effective service implementation. Exploring the potential of Semantic Communications (SemCom) to enhance point-to-point physical layer efficiency shows great promise in addressing this challenge. However, achieving efficient SemCom requires overcoming the significant hurdle of knowledge sharing between semantic decoders and encoders, particularly in the dynamic and non-stationary environment with stringent end-to-end quality requirements. To bridge this gap in existing literature, this paper introduces the Knowledge Base Management And Orchestration (KB-MANO) framework. Rooted in the concepts of Computing-Network Convergence (CNC) and lifelong learning, KB-MANO is crafted for the allocation of network and computing resources dedicated to updating and redistributing KBs across the system. The primary objective is to minimize the impact of knowledge management activities on actual service provisioning. A proof-of-concept is proposed to showcase the integration of KB-MANO with resource allocation in radio access networks. Finally, the paper offers insights into future research directions, emphasizing the transformative potential of semantic-oriented communication systems in the realm of 6G technology. |
| title | Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2407.00081 |