Using Models of Parallel Specialized Processors to Solve the Problem of Signal Separation

Abstract

This paper considers models of highly efficient specialized processors used for parallel data processing as part of solving the problem of extracting individual signals from an additive mixture of several signals. The proposed models of recursive, nonrecursive, and regularization-based parallel specialized processors provide versatility in solving the problem of signal separation with various algorithms. An advantage of regularization-based processors is that they make the solution stable under conditions where the parameters of objects exhibit expected uncertainty when the inverse problem of signal separation is ill-posed. This paper presents the results we obtained from an asymptotic analysis of the computational complexity involved. The results identify the time it takes to solve problems by using specialized processors. The paper also identifies the conditions for the efficient use of specialized processors.