Kavli Affiliate: Salman Habib
| First 5 Authors: Salman Habib, Remi Chou, Taejoon Kim, ,
| Summary:
The reconstruction of sparse signals from a limited set of measurements poses
a significant challenge as it necessitates a solution to an underdetermined
system of linear equations. Compressed sensing (CS) deals with sparse signal
reconstruction using techniques such as linear programming (LP) and iterative
message passing schemes. The interval passing algorithm (IPA) is an attractive
CS approach due to its low complexity when compared to LP. In this paper, we
propose a sequential IPA that is inspired by sequential belief propagation
decoding of low-density-parity-check (LDPC) codes used for forward error
correction in channel coding. In the sequential setting, each check node (CN)
in the Tanner graph of an LDPC measurement matrix is scheduled one at a time in
every iteration, as opposed to the standard “flooding” interval passing
approach in which all CNs are scheduled at once per iteration. The sequential
scheme offers a significantly lower message passing complexity compared to
flooding IPA on average, and for some measurement matrix and signal sparsity, a
complexity reduction of 36% is achieved. We show both analytically and
numerically that the reconstruction accuracy of the IPA is not compromised by
adopting our sequential scheduling approach.
| Search Query: ArXiv Query: search_query=au:”Salman Habib”&id_list=&start=0&max_results=3