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The authors examine the problem of determining which nodes are neighbors of a given one in a wireless network. They consider an unsupervised network operating on a frequency-flat Gaussian channel, where K + 1 nodes associate their identities to non-orthogonal 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. Of late, wireless networks, and in particular sensor networks, have been the object of a good deal of interest, also spurred by the manifold applications they can be associated.
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