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Blind MIMO system identification based on cumulant subspace decomposition

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2 Author(s)
Jing Liang ; Silicon Labs. Inc., Broomfield, CO, USA ; Zhi Ding

Blind identification of multiple-input multiple-output (MIMO) linear systems can be achieved by utilizing higher order statistics of the output signals. We study the blind identification of MIMO systems whose inputs are mutually independent, temporally white, non-Gaussian source signals. Based on sub-space analysis, we develop a new linear batch algorithm to identify MIMO systems from the common space of a set of fourth-order cumulant matrices of the channel outputs. Given knowledge of the channel orders, the identifiability conditions required by the proposed algorithm are properly established. Like most subspace-based approaches, this new algorithm remains sensitive to channel order overestimation. Simulation results illustrate its performance for various channel models.

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Signal Processing, IEEE Transactions on  (Volume:51 ,  Issue: 6 )