Blind Identification of Nonlinear Finite Impulse Response Volterra Channels
Fang Yangwang¹, Jiao Licheng¹, Han Chongzhao²
(1. Xidian University, Xi'an 710071, China; 2. Xi'an Jiaotong University)

Abstract: A subspace approach to blind identification and equalization of a nonlinear single-input multiple-output (SIMO) finite impulse response (FIR) Volterra system is proposed. Firstly, a SIMO nonlinear Volterra channel model is described, and is transformed into a MIMO blind channel model. Then, the blind identificable conditions for FIR Volterra system are discussed and a subspace approach to blind identification of SIMO Volterra system is given; furthermore, the deterministic blind equalization of Volterra FIR nonlinear channel is studied. Finally, the validity of this method is verified by a simulation example. The advantage of this method is that it requires less restriction conditions, i.e., it only requires that the auto-correlation matrix of an input signal is non-singular and can work well in the case of low SNR in comparison with the deterministic approach.
Keywords: blind identification;FIR Volterra channel;subspace method