  Machine learning (ML) and tensor-based methods have been of significant
interest for the scientific community for the last few decades. In a previous
work we presented a novel tensor-based system identification framework to ease
the computational burden of tensor-only architectures while still being able to
achieve exceptionally good performance. However, the derived approach only
allows to process real-valued problems and is therefore not directly applicable
on a wide range of signal processing and communications problems, which often
deal with complex-valued systems. In this work we therefore derive two new
architectures to allow the processing of complex-valued signals, and show that
these extensions are able to surpass the trivial, complex-valued extension of
the original architecture in terms of performance, while only requiring a
slight overhead in computational resources to allow for complex-valued
operations.
