Complex-valued Adaptive System Identification via Low-Rank Tensor Decomposition
This work addresses complex-valued system identification for signal processing and communications, but it is incremental as it extends an existing method.
The paper tackles the limitation of a previous tensor-based system identification framework that only handled real-valued problems, by deriving two new architectures for complex-valued signals, which outperform a trivial complex extension with only a slight computational overhead.
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.