Transformer-Based Link Failure Detection in 5G Cellular Networks

Radio Link Failure (RLF) detection in Radio Access Networks (RANs) is crucial for ensuring seamless communication in 5G networks. Nonetheless, current approaches based on traditional Machine Learning (ML) algorithms fail to find a trade-off between accuracy and computational complexity. Thus, in this paper, we explore advanced and computationally efficient types of transformers, such as Linformer and Performer. These models significantly reduce computational complexity while maintaining strong performance by leveraging different attention mechanisms. Extensive evaluations using a realistic dataset under various percentages of link failures show that Linformer provides a favourable balance between accuracy and training time.