  We examine the problem of determining which nodes are neighbors of a given
one in a wireless network. We consider an unsupervised network operating on a
frequency-flat Gaussian channel, where $K+1$ nodes associate their identities
to nonorthogonal signatures, transmitted at random times, synchronously, and
independently. A number of neighbor-discovery algorithms, based on different
optimization criteria, are introduced and analyzed. Numerical results show how
reduced-complexity algorithms can achieve a satisfactory performance.
