Federated Variational Autoencoder and Transformers for Temporal Data Augmentation and Interference Detection in Heterogeneous 5G Networks

In this paper, we address passive intermodulation (PIM) interference in heterogeneous fifth-generation (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 G}$</tex>) networks, compounded by limited datasets and non-independent, non-identically distributed (non-IID) data across cells. Though uncommon, PIM events can significantly degrade the signal-to-interference-plus-noise ratio (SINR), making their timely detection essential. Newly deployed cells often lack sufficient PIM samples to train a PIM detector, while existing cells face imbalances between PIM and no PIM data, complicating deep learning (DL) model training. The varying data distribution across cells adds further complexity. We propose a Federated Variational Autoencoder with Averaging Filter (FVAE-AvF) framework to address data heterogeneity, scarcity, and imbalanced data across cells in 5G networks. The Temporal Augmentation Variational Long Short-Term Memory (TAVLNet) serves as the local model, generating temporally coherent synthetic PIM data, while an Averaging Filter (AvF) ensures smoother data generation. For PIM detection, Vision Transformers (ViTs) are employed. Training them on the synthetic data generated from the FVAEAvF significantly improves detection accuracy and robustness. Leveraging federated learning (FL), the framework enables training without transferring data to a central location reducing data transfer costs and enhancing performance, particularly in managing non-IID data across distributed networks. It also allows cells with limited PIM data to benefit from the training on data from other cells, ensuring more effective model development. Numerical results show that the FVAE-AvF framework handles heterogeneous non-IID data better, improves detector performance with generated data, and enhances network robustness compared to existing FL and data augmentation methods.