Variational inference -based em for quantized FIR system parameter identification

In this paper, we propose a general method based on variational inference for the system parameter (SP) identification of quantized finite impulse response (FIR) systems. The core is to first express the transition probability from actual data to quantized output with a simple general Gaussian process characterized by compensation parameters (CPs). Then the SP and CPs are iteratively estimated by proposing an expectation maximization (EM) based on variational inference (EM-VI). The EM-VI avoids the complex integral calculation, but with good SP identification performance. Finally, the simulation demonstrates that the EM-VI is feasible and effective with the almost same accuracy as the existing algorithms, but requires the less computation.