Blind Deconvolution via Sequential Imputations

Abstract

The sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive Gaussian noises, a problem known as blind deconvolution in digital communication. A rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified. The proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration. Two simulated systems are studied to illustrate the proposed method. Our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling.

Key Words:

• Bayesian model
• Communication
• Gibbs sampling
• Importance sampling
• Predictive distribution
• Signal transmission