Reduced Complexity Blind Estimation of Under-Determined Convolutive Mimo Systems
Yuanning Yu, Athina P. Petropulu · 2006
We consider identification of an under-determined convolutive multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In our recent work, we showed that an Ni-input and No-output system can be estimated within trivial ambiguities based on PARAFAC decomposition of a tensor containing K-th order statistics of the system output, where Kgesmax{(2Ni-1)/(No-1), 3}. In this paper we show that by using a tensor pair we can guarantee identifiability while using statistics of order smaller than in the single tensor case. We also provide an iterative identification scheme. The proposed tensor-pair approach results in complexity reduction as it involves lower dimensionality tensors and lower order statistics