Signal estimation and filtering from quantized observations via adaptive stochastic resonance

Using a gradient-based algorithm, we investigate signal estimation and filtering in a large-scale summing network of single-bit quantizers. Besides adjusting weights, the proposed learning algorithm also adaptively updates the level of added noise components that are intentionally injected into quantizers. Experimental results show that minimization of the mean-squared error requires a nonzero optimal level of the added noise. The process adaptively achieves in this way a form of stochastic resonance or noise-aided signal processing. This adaptive optimization method of the level of added noise extends the application of adaptive stochastic resonance to some complex nonlinear signal processing tasks.

Automated summary

The research focuses on signal estimation and filtering in a large-scale summing network of single-bit quantizers using a gradient-based algorithm. Experimental results indicate that minimizing mean-squared error requires an optimal level of added noise. This adaptive optimization method of the level of added noise extends the application of adaptive stochastic resonance to complex nonlinear signal processing tasks.