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

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, namelymodulated 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.