Spectrum Recommendation in High Frequency Communications: A Knowledge Inference Method
Jiachen Sun, Guoru Ding, Yehui Song · 2023
As an intelligent approach for efficient spectrum utilization, spectrum recommendation is significant but largely unexplored, especially in the few-shot scenario of inference on unknown channel qualities in High-Frequency (HF) communication. For the incompetence of data-driven approaches, we consider playing a guiding role of domain knowledge of HF. Firstly, a Knowledge Graph (KG) for spectrum recommendation is developed to organize and represent the propagation knowledge of HF, and modeled as an undirected graph for the convenience of extracting and reasoning knowledge. Inference on channel qualities for recommendation is further converted to the node regression problem on the undirected graph. Then, inference schemes based on Graph Convolutional Network (GCN) are proposed to solve the node regression problem, where we transfer knowledge extracted from the complete data of the source domain to the few-shot inference scenario of the target domain. Simulation experiments demonstrate the effectiveness of the proposed GCN-based inference algorithm with knowledge transfer.