Semantic Cognitive Network Based on Distributed Knowledge Reasoning
The emergence of a new vision and demand for the sixth-generation(6G)wireless network,characterized by"services on demand,networks adapting to needs,and resources shared at will",has sparked a new communication paradigm-the development of se-mantic communication and cognitive semantic networks.Semantic communication enhances communication efficiency and reliability by transmitting the true meaning of information rather than transmitting and reproducing complete original messages.Deploying and fully re-alizing the potential of semantic communication in 6G networks requires a new technology capable of effectively processing and utilizing semantic information.In this paper,we propose a novel framework for 6G network semantic communication based on graph reasoning and federated learning.The proposed framework combines graph reasoning techniques such as graph neural networks and knowledge graph embeddings to achieve efficient and scalable reasoning over large-scale and complex semantic knowledge bases.Additionally,the frame-work integrates federated learning techniques,enabling collaborative and privacy-preserving reasoning across multiple edge servers while keeping sensitive and personal data retained on edge servers.We conduct extensive experiments to evaluate the performance of the pro-posed framework in terms of inference accuracy,efficiency,and scalability,demonstrating its superiority over existing methods.Further-more,the framework opens up new research directions in the fields of semantic communication,graph reasoning,and federated edge computing,which are crucial for realizing the vision of an intelligent endogenous communication network for 6G.This paper proposes a new framework for semantic cognitive networks based on distributed knowledge reasoning,aiming to address the challenges of achieving more intelligent,efficient,and adaptive network management and control.The framework integrates advanced graph reasoning techniques such as graph neural networks and knowledge graph embedding,enabling efficient and scalable reasoning on large-scale and complex semantic knowledge bases.Additionally,the framework combines federated learning techniques to achieve col-laborative and privacy-preserving reasoning across multiple edge servers.The main contributions of this paper include the proposal of the aforementioned framework and the integrated development of graph reasoning techniques and federated learning techniques adapted to this framework.Extensive experimental evaluations demonstrate that the proposed framework outperforms existing methods in terms of reasoning accuracy,efficiency,and scalability.
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