无线数据知识图谱的空时异构表示学习方法  

A Spatiotemporal Heterogeneous Representation Learning Approach for Wireless Data Knowledge Graphs

作　　者：冯庆霞 杨绿溪[1] FENG Qingxia;YANG Luxi(School of Information Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China)

机构地区：[1]东南大学信息科学与工程学院,江苏南京210096

出　　处：《信号处理》2025年第4期749-758,共10页Journal of Signal Processing

基　　金：国家重点研发计划项目(2024YFE0200700);江苏省前沿引领技术基础研究重大项目(BK20222001);国家自然科学基金(61971128)。

摘　　要：在智能应用的推动下,用户对通信性能的要求已超出现有网络的能力。然而,在无线系统产生的海量通信信号中,仅少量字段对网络性能具有关键影响,如何有效挖掘数据仍面临挑战。现有的大多数研究忽略了无线数据的多源异构性和时序特性,难以揭示通信网络的运行机制及优化潜力。为此,本文提出了一种空时异构表示学习模型,针对无线数据知识图谱的空时异构特性,深入挖掘数据字段间的关系并优化无线通信网络性能。具体而言,本文将无线数据知识图谱的实体和关系转化为异构图中的节点和边,并通过学习节点的特征表示向量,全面揭示无线数据字段间的相互关系。这些关系不仅揭示了数据字段间的物理关联,还为性能优化提供了理论依据。为提升模型的表达能力,本文通过设计特征图模块,利用多跳连接子图表征异构节点的结构和属性信息。此外,本文开发了空时动态模块,通过联合聚合相邻节点的结构和时间信息,从而学习有效的节点表示向量。与现有方法相比,本文提出的模型在理解无线数据字段相互关系及性能预测等方面表现出显著优势,并在吞吐量预测任务中展现了实际应用价值。本文的研究不仅弥补了现有研究在无线数据知识图谱表示学习领域的不足,还为无线通信系统的优化提供了理论和实践支持。Driven by intelligent applications,the demand for enhanced communication performance has exceeded the capabilities of existing networks.However,the limitations of existing network architectures hinder their ability to adapt dynamically to the continuously evolving application requirements and complexities of a changing network environment.This poses unprecedented challenges for traditional rule-based algorithms.In contrast,artificial intelligence models,known for their exceptional data-fitting capabilities,offer distinct advantages in addressing these challenges.By leveraging artificial intelligence models,it is possible to effectively analyze and optimize the intricate operational frameworks of wireless communication networks,thereby enabling more resilient and adaptive 6G network systems.The large number of communication signals generated by complex wireless systems contain numerous fields,yet only a limited subset significantly impacts network performance.Consequently,artificial intelligence models must identify and analyze the interrelationships among these data fields to enhance the efficiency of wireless communication networks.Existing methods for wireless data analysis often overlook the multi-source heterogeneity and temporal dynamics of such data.As an emerging artificial intelligence technology,knowledge graphs can integrate expert knowledge with data awareness to more effectively represent the correlations among data.In this study,we developed a representation learning model tailored to the spatiotemporal heterogeneous nature of the wireless data knowledge graph,referred to as the spatiotemporal heterogeneous representation learning model.Specifically,the proposed approach transforms the entities and relationships in a wireless data knowledge graph into nodes and edges of a heterogeneous graph,learning node feature representations to systematically capture the interdependencies among wireless data fields.These relationships not only reveal the physical associations between data fields but also serve as

关 键 词：无线通信信号 知识图谱 图神经网络 

分 类 号：TN929.5[电子电信—通信与信息系统]