Cross-Comparison of Neural Architectures and Data Sets for Digital Self-Interference Modeling

Abstract:
Inband full-duplex communication requires accurate modeling and cancellation of self-interference, specifically in the digital domain. Neural networks are presently candidate models for capturing nonlinearity of the self-interference path. This work utilizes synthetic and real data from different sources to evaluate and cross-compare performances of previously proposed neural self-interference models from different sources. The relevance of the analysis consists in the mutual assessment of methods on data they were not specifically designed for. We find that our previously proposed Hammerstein model represents the range of data sets well, while being significantly smaller in terms of the number of parameters. A new Wiener-Hammerstein model further enhances the generalization performance.