# Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions

arXiv: https://arxiv.org/abs/2407.00081 · HTML: https://mlsanity.com/paper/2407.00081/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022)
- [Learning Semantics: An Opportunity for Effective 6G Communications](https://mlsanity.com/paper/2110.08049/) (2021)
- [A Survey on Semantic Communications for Intelligent Wireless Networks](https://mlsanity.com/paper/2202.03705/) (2022)
- [Engineering Semantic Communication: A Survey](https://mlsanity.com/paper/2208.06314/) (2022)
- [Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication](https://mlsanity.com/paper/2303.10867/) (2023)
- [Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference](https://mlsanity.com/paper/2302.14702/) (2023)
- [Open RAN meets Semantic Communications: A Synergistic Match for Open, Intelligent, and Knowledge-Driven 6G](https://mlsanity.com/paper/2310.09951/) (2023)
- [A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications](https://mlsanity.com/paper/2401.06308/) (2024)
- [Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)](https://mlsanity.com/paper/2403.07573/) (2024)

## Built on by (most cited first, 4 of 4)

- [On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks](https://mlsanity.com/paper/2409.15246/) (2024)
- [An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning](https://mlsanity.com/paper/2502.16198/) (2025)
- [Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.01924/) (2024)
- [Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6G](https://mlsanity.com/paper/2606.31715/) (2026)

## Abstract (arXiv)

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.