Deep unfolding based hyper‐parameter optimisation for self‐interference cancellation in LTE‐A/5G‐transceivers

Abstract Deep unfolding is a very promising concept that allows to combine the advantages of traditional estimation techniques, such as adaptive filters, and machine learning approaches, like artificial neural networks. Focusing on a challenging self‐interference problem occurring in frequency‐division duplex radio frequency transceivers, namely modulated spurs, it is shown that deep unfolding enables remarkable performance gains. Based on the hyper‐parameter optimisation of several least‐mean squares (LMS) variants and the recursive‐least squares algorithm, the importance of a well‐chosen loss function are highlighted. Especially the variable step‐size LMS and the transform‐domain LMS vastly benefit without increased runtime complexity.

Abstract licence: CC BY-ND

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