An immune-inspired, information-theoretic framework for blind inversion of Wiener systems

This work proposes a new approach to the blind inversion of Wiener systems. A Wiener system is composed of a linear time-invariant (LTI) sub-system followed by a memoryless nonlinear function. The goal is to recover the input signal by knowing just the output of the Wiener system, and the straightforward scheme to achieve this is called the Hammerstein system –– apply a memoryless nonlinear mapping followed by a LTI sub-system to the output signal of the Wiener system. If the input of the Wiener system is originally iid and some mild conditions are satisfied, the inversion is possible. Based on this statement and the limitations of relevant previous works, a solution is proposed combining (i) immune-inspired optimization algorithms, (ii) information theory and (iii) IIR filters that yield a robust scheme with a relatively reduced risk of local convergence. Experimental results indicated a similar or superior performance of the new approach, in comparison with two other blind methodologies.