  Nanoscale wireless sensor networks (NWSNs) could be within reach soon using
graphene-based antennas, which resonate in 0.1-10 terahertz band. To conserve
the limited energy available at nanoscale, it is expected that NWSNs will
communicate using extremely short pulses on the order of femtoseconds. Accurate
estimation of direction of arrival (DOA) for such terahertz pulses will help
realize many useful applications for NWSNs. In this paper, using the well-known
MUltiple SIgnal Classification (MUSIC) algorithm, we study DOA estimation for
NWSNs for different energy levels, distances, pulse shapes, and frequencies.
Our analyses reveal that the best DOA estimation is achieved with the first
order Gaussian pulses, which emit their peak energy at 6 THz. Based on Monte
Carlo simulations, we demonstrate that MUSIC algorithm is capable of estimating
DOA with root mean square error less than one degree from a distance of around
6 meter for pulse energy as little as 1 atto Joule.
