Neighbor Discovery in Wireless Networks:A Multiuser-Detection Approach

TL;DR

This paper introduces and analyzes multiple neighbor-discovery algorithms for wireless networks operating on Gaussian channels, demonstrating that simplified algorithms can perform effectively in identifying neighboring nodes.

Contribution

It presents new neighbor-discovery algorithms based on various optimization criteria for unsupervised wireless networks with nonorthogonal signatures.

Findings

Reduced-complexity algorithms achieve satisfactory performance

Algorithms are effective in unsupervised Gaussian channel environments

Analysis includes multiple optimization-based approaches

Abstract

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.