  Practical constructions of lossless distributed source codes (for the
Slepian-Wolf problem) have been the subject of much investigation in the past
decade. In particular, near-capacity achieving code designs based on LDPC codes
have been presented for the case of two binary sources, with a binary-symmetric
correlation. However, constructing practical codes for the case of non-binary
sources with arbitrary correlation remains by and large open. From a practical
perspective it is also interesting to consider coding schemes whose performance
remains robust to uncertainties in the joint distribution of the sources.
  In this work we propose the usage of Reed-Solomon (RS) codes for the
asymmetric version of this problem. We show that algebraic soft-decision
decoding of RS codes can be used effectively under certain correlation
structures. In addition, RS codes offer natural rate adaptivity and performance
that remains constant across a family of correlation structures with the same
conditional entropy. The performance of RS codes is compared with dedicated and
rate adaptive multistage LDPC codes (Varodayan et al. '06), where each LDPC
code is used to compress the individual bit planes. Our simulations show that
in classical Slepian-Wolf scenario, RS codes outperform both dedicated and
rate-adaptive LDPC codes under $q$-ary symmetric correlation, and are better
than rate-adaptive LDPC codes in the case of sparse correlation models, where
the conditional distribution of the sources has only a few dominant entries. In
a feedback scenario, the performance of RS codes is comparable with both
designs of LDPC codes. Our simulations also demonstrate that the performance of
RS codes in the presence of inaccuracies in the joint distribution of the
sources is much better as compared to multistage LDPC codes.
