Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions
- URL: http://arxiv.org/abs/2407.00081v1
- Date: Mon, 24 Jun 2024 09:04:09 GMT
- Title: Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions
- Authors: Masoud Shokrnezhad, Hamidreza Mazandarani, Tarik Taleb, Jaeseung Song, Richard Li,
- Abstract summary: This paper introduces the Knowledge Base Management And Orchestration (KB-MANO) framework.
KB-MANO is crafted for the allocation of network and computing resources dedicated to updating and redistributing knowledge.
A proof-of-concept is proposed to showcase the integration of KB-MANO with resource allocation in radio access networks.
- Score: 16.807697160355303
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Emergency Computing: An Adaptive Collaborative Inference Method Based on
Hierarchical Reinforcement Learning [14.929735103723573]
We propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I)
The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment.
We specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning.
arXiv Detail & Related papers (2024-02-03T13:28:35Z) - Will 6G be Semantic Communications? Opportunities and Challenges from
Task Oriented and Secure Communications to Integrated Sensing [49.83882366499547]
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) networks through the integration of multi-task learning.
We employ deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver.
We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases.
arXiv Detail & Related papers (2024-01-03T04:01:20Z) - Adaptive Resource Allocation for Semantic Communication Networks [34.189531352110386]
This paper investigates the quality of service for semantic communication networks, including the semantic quantization efficiency (SQE) and transmission latency.
A problem maximizing the overall effective SC-QoS is formulated by jointly the transmit beamforming the base station, the bits semantic representation the subchannel assignment, and the semantic resource allocation.
Our design can effectively combat semantic noise and achieve superior performance in wireless communications compared to several benchmark schemes.
arXiv Detail & Related papers (2023-12-02T09:12:12Z) - A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication [53.78269720999609]
Generative AI applications have been recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC)
It is challenging to provide qualified AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources.
We propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework.
arXiv Detail & Related papers (2023-10-26T18:05:22Z) - Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts [89.04751776308656]
This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
arXiv Detail & Related papers (2023-09-05T23:24:56Z) - Machine Learning-Based User Scheduling in Integrated
Satellite-HAPS-Ground Networks [82.58968700765783]
Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G)
This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
arXiv Detail & Related papers (2022-05-27T13:09:29Z) - Intelligent Transportation Systems' Orchestration: Lessons Learned &
Potential Opportunities [5.012225318994545]
6G is being proposed as the set of technologies and architectures that will achieve this target.
Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs)
One prominent challenge is ITS orchestration due to the various supporting technologies and heterogeneous networks used to offer the desired ITS applications/services.
arXiv Detail & Related papers (2022-05-05T15:41:43Z) - A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning [115.75967665222635]
Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
arXiv Detail & Related papers (2020-09-13T14:53:01Z) - Communication-Efficient and Distributed Learning Over Wireless Networks:
Principles and Applications [55.65768284748698]
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
arXiv Detail & Related papers (2020-08-06T12:37:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.