Learning Wireless Data Knowledge Graph for Green Intelligent
  Communications: Methodology and Experiments
Yongming Huang Xiaohu You Hang Zhan Shiwen He Ningning Fu Wei Xu
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.