This report discusses the design of three novel, cross-layer neighbor discovery schemes for directional wireless mesh networks, which have significantly lower protocol overhead and discovery latency. These schemes intelligently consider the collisions among the neighbor discovery messages of the neighboring nodes and use machine learning techniques. The performance of these schemes is evaluated for different network sizes, node densities, beamwidth and number of one-hop neighbors. Finally, implementation of directional neighbor discovery schemes in a hardware testbed is discussed.
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