A Novel Adaptive Scenario Classification Algorithm for 6G Wireless Communications

The current propagation scenario classification methods of standardized channel models are rough and the definitions are unclear, despite the diverse propagation scenarios in the sixth-generation (6G) communications technology era. This paper proposes an unsupervised classification algorithm based on the self-organizing map (SOM) to establish a more refined propagation scenario classification for the 6G era. First, the physical environment information of the actual scenario is extracted based on digital maps. Then, the SOM neural network parameters are determined and the propagation scenario classification is implemented. Finally, the effectiveness of the proposed propagation scenario classification is verified by the data collected from the practical communication network in the following three aspects: physical environment characteristics, evaluation indicators, and channel characteristics. The results show that the proposed SOM scenario classification algorithm outperforms K-means, DBSCAN, and the Gaussian mixture model (GMM), among other algorithms, providing an effective solution for the detailed classification of 6G propagation scenarios.