We present a mathematical connection between channel coding and compressed
sensing. In particular, we link, on the one hand, \emph{channel coding linear
programming decoding (CC-LPD)}, which is a well-known relaxation of
maximum-likelihood channel decoding for binary linear codes, and, on the other
hand, \emph{compressed sensing linear programming decoding (CS-LPD)}, also
known as basis pursuit, which is a widely used linear programming relaxation
for the problem of finding the sparsest solution of an under-determined system
of linear equations.
In this paper we study the decoding capabilities of convolutional codes over
the erasure channel. Of special interest will be maximum distance profile (MDP)
convolutional codes. These are codes which have a maximum possible column
distance increase. We show how this strong minimum distance condition of MDP
convolutional codes help us to solve error situations that maximum distance
separable (MDS) block codes fail to solve. Towards this goal, we define two
subclasses of MDP codes: reverse-MDP convolutional codes and complete-MDP
convolutional codes.
In this paper we analyze the bound on the additive white Gaussian noise
channel (AWGNC) pseudo-weight of a (c,d)-regular linear block code based on the
two largest eigenvalues of H^T H. In particular, we analyze (c,d)-regular
quasi-cyclic (QC) codes of length rL described by J x L block parity-check
matrices with circulant block entries of size r x r.