Intelligent communications have played a pivotal role in shaping the
evolution of 6G networks. Native artificial intelligence (AI) within green
communication systems must meet stringent real-time requirements. To achieve
this, deploying lightweight and resource-efficient AI models is necessary.
However, as wireless networks generate a multitude of data fields and
indicators during operation, only a fraction of them imposes significant impact
on the network AI models. Therefore, real-time intelligence of communication
systems heavily relies on a small but critical set of the data that profoundly
influences the performance of network AI models. These challenges underscore
the need for innovative architectures and solutions. In this paper, we propose
a solution, termed the pervasive multi-level (PML) native AI architecture,
which integrates the concept of knowledge graph (KG) into the intelligent
operational manipulations of mobile networks, resulting in the establishment of
a wireless data KG. Leveraging the wireless data KG, we characterize the
massive and complex data collected from wireless communication networks and
analyze the relationships among various data fields. The obtained graph of data
field relations enables the on-demand generation of minimal and effective
datasets, referred to as feature datasets, tailored to specific application
requirements. Consequently, this architecture not only enhances AI training,
inference, and validation processes but also significantly reduces resource
wastage and overhead for communication networks. To implement this
architecture, we have developed a specific solution comprising a
spatio-temporal heterogeneous graph attention neural network model (STREAM) as
well as a feature dataset generation algorithm. Experiments are conducted to
validate the effectiveness of the proposed architecture.

通过将知识图谱的概念融入移动网络的智能操作中，建立了一个无线数据知识图谱，从而实现对从无线通信网络收集的海量和复杂数据的特征数据集的按需生成，提高了人工智能训练、推理和验证过程的效率，并显著减少了通信网络的资源浪费和开销。

无线数据知识图谱的学习与绿色智能通信的方法与实验

Learning Wireless Data Knowledge Graph for Green Intelligent  Communications: Methodology and Experiments

It has been 20 years since the concept of cognitive radio (CR) was proposed,
which is an efficient approach to provide more access opportunities to connect
massive wireless devices. To improve the spectrum efficiency, CR enables
unlicensed usage of licensed spectrum resources. It has been regarded as the
key enabler for intelligent communications. In this article, we will provide an
overview on the intelligent communication in the past two decades to illustrate
the revolution of its capability from cognition to artificial intelligence
(AI). Particularly, this article starts from a comprehensive review of typical
spectrum sensing and sharing, followed by the recent achievements on the
AI-enabled intelligent radio. Moreover, research challenges in the future
intelligent communications will be discussed to show a path to the real
deployment of intelligent radio. After witnessing the glorious developments of
CR in the past 20 years, we try to provide readers a clear picture on how
intelligent radio could be further developed to smartly utilize the limited
spectrum resources as well as to optimally configure wireless devices in the
future communication systems.

本文概述了过去 20 年中智能通信的发展，从认知无线电到人工智能辅助的智能无线电，评估了智能通信的研究挑战，展示了更智能地利用有限频谱资源以及在未来通信系统中优化配置无线设备的道路。