Compressive Sensing, Quantization, and Message Passing
Ulugbek Kamilov, EPFL STI LIB

Seminar • 20 June 2011 • BM 4.233

Abstract
Message-passing algorithms on graphical models proved to be effective in several estimation problems. In this talk we present Generalized Approximate Message Passing (GAMP) algorithm which was recently developed for compressive sensing estimation with arbitrary noisy distributions. The algorithm can be analyzed using state evolution recursion, which can predict error performance of the GAMP. We present application of this algorithm for compressive sensing estimation with quantized measurements and demonstrate its superior performance to other standard algorithms via numerical simulations.