An AI-Based Optimization Method for Atmospheric Waveguide Interference

Atmospheric waveguide interference (AWI) is one of the typical interferences in TDD networks, with a wide impact range and rapid deterioration of business perception. This article introduces an AI-based clustering detection and optimization method for AWI. The scheme is based on the RIM interference feature evaluation model of TDD base stations and the Wkmeans clustering algorithm, which realizes the clustering identification and detection of interference cells in different scenarios, thereby assisting operators in implementing optimization strategies, alleviating business problems and user complaints caused by interference.