基于分布式知识推理的语义认知网络

Semantic Cognitive Network Based on Distributed Knowledge Reasoning

6G无线网络“服务随心所想、网络随需而变、资源随愿共享”的全新愿景与需求,激发了一种新的通信范式——语义通信和语义认知网络的发展。语义通信通过传输信息的真实含义而非传输和复现完整的原始消息来提升通信效率和可靠性。要在6G网络中部署和充分发挥语义通信的潜力,需要一种能够有效处理和利用语义信息的新技术。提出了一种基于图推理和联邦学习的6G网络语义通信新框架,框架结合了图推理技术,例如图神经网络和知识图嵌入,以实现对大规模和复杂语义知识库的高效且可扩展的推理;框架集成了联邦学习技术,可以跨多个边缘服务器进行协作和隐私保护推理,同时将敏感数据和个人数据保留在边缘服务器上。进行广泛的实验,以评估所提框架在推理准确性、效率和可扩展性方面的性能,并证明其相对于现有方法的优越性。框架在语义通信、图推理和联合边缘计算领域开辟了新的研究方向,对实现6G智能内生的通信网络的愿景至关重要。

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 semantic 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 realizing 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 framework 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 proposed framework in terms of inference accuracy,efficiency,and scalability,demonstrating its superiority over existing methods.Furthermore,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 collaborative 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.