Approximate message passing under finite alphabet constraints

Andreas C Muller, Dino Sejdinovic, Robert J Piechocki

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)


In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Publication statusPublished - 2012


Dive into the research topics of 'Approximate message passing under finite alphabet constraints'. Together they form a unique fingerprint.

Cite this