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

arXiv: https://arxiv.org/abs/2404.10365 · HTML: https://mlsanity.com/paper/2404.10365/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [When Machine Learning Meets Big Data: A Wireless Communication Perspective](https://mlsanity.com/paper/1901.08329/) (2019)
- [True-data Testbed for 5G/B5G Intelligent Network](https://mlsanity.com/paper/2011.13152/) (2020)
- [Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks](https://mlsanity.com/paper/2201.12899/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)

## Abstract (arXiv)

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.