  We study the reconstruction of discrete-valued sparse signals from
underdetermined systems of linear equations. On the one hand, classical
compressed sensing (CS) is designed to deal with real-valued sparse signals. On
the other hand, algorithms known from MIMO communications, especially the
sphere decoder (SD), are capable to reconstruct discrete-valued non-sparse
signals from well- or overdefined system of linear equations. Hence, a
combination of both approaches is required. We discuss strategies to include
the knowledge of the discrete nature of the signal in the reconstruction
process. For brevity, the exposition is done for combining the orthogonal
matching pursuit (OMP) with the SD; design guidelines are derived. It is shown
that by suitably combining OMP and SD an efficient low-complexity scheme for
the detection of discrete sparse signals is obtained.
