1 research outputs found
Algebraic Optimization of Binary Spatially Coupled Measurement Matrices for Interval Passing
We consider binary spatially coupled (SC) low density measurement matrices
for low complexity reconstruction of sparse signals via the interval passing
algorithm (IPA). The IPA is known to fail due to the presence of harmful
sub-structures in the Tanner graph of a binary sparse measurement matrix, so
called termatiko sets. In this work we construct array-based (AB) SC sparse
measurement matrices via algebraic lifts of graphs, such that the number of
termatiko sets in the Tanner graph is minimized. To this end, we show for the
column-weight-three case that the most critical termatiko sets can be removed
by eliminating all length-12 cycles associated with the Tanner graph, via
algebraic lifting. As a consequence, IPA-based reconstruction with SC
measurement matrices is able to provide an almost error free reconstruction for
significantly denser signal vectors compared to uncoupled AB LDPC measurement
matrices.Comment: 5 pages, 2 figures, To appear in the Proceedings of 2018 IEEE
Information Theory Workshop, Guangzhou, Chin