  5G will enable the growing demand for Internet of Things (IoT),
high-resolution video streaming, and low latency wireless services. Demand for
such services is expected to growth rapid, which will require a search for
Beyond 5G technological advancements in wireless communications. Part of these
advancements is the need for additional spectrum, namely moving toward the
terahertz (THz) range. To compensate for the high path loss in THz, narrow
beamwidths are used to improve antenna gains. However, with narrow beamwidths,
even minor fluctuations in device location (such as through body movement) can
cause frequent link failures due to beam misalignment. In this paper, we
provide a solution to these small-scale indoor movement that result in
mobility-induced outages. Like a moth randomly flutters about, Mobility-induced
Outages in THz (MOTH) can be ephemeral in nature and hard to avoid. To deal
with MOTH we propose two methods to predict these outage scenarios: (i)
Align-After-Failure (AAF), which predicts based on fixed time margins, and (ii)
Align-Before-Failure (ABF), which learns the time margins through user mobility
patterns. In this paper, two different online classifiers were used to train
the ABF model to predicate if a mobility-induced outage is going to occur;
thereby, significantly reducing the time spent in outage scenarios. Simulation
results demonstrate a relationship between optimal beamwidth and human mobility
patterns. Additionally, to cater to a future with dense deployment of Wireless
Personal Area Network (WPAN), it is necessary that we have efficient deployment
of resources (e.g., THz-APs). One solution is to maximize the user coverage for
a single AP, which might be dependent on multiple parameters. We identify these
parameters and observe their tradeoffs for improving user coverage through a
single THz-AP.
