Identification of Widely Linear Systems Using Data-Dependent Superimposed Training
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DOI: 10.1109/lsp.2022.3212959
Abstract: In this correspondence, we address the identification of widely linear (WL) systems using data-dependent superimposed training (DDST). The analysis shows that the nonlinear nature of WL systems can be exploited to decouple the finite impulse… read more here.
Keywords: data dependent; identification widely; using data; widely linear ... See more keywords