  Beamforming techniques are considered as essential parts to compensate the
severe path loss in millimeter-wave (mmWave) communications by adopting large
antenna arrays and formulating narrow beams to obtain satisfactory received
powers. However, performing accurate beam alignment over such narrow beams for
efficient link configuration by traditional beam selection approaches, mainly
relied on channel state information, typically impose significant latency and
computing overheads, which is often infeasible in vehicle-to-vehicle (V2V)
communications like highly dynamic scenarios. In contrast, utilizing
out-of-band contextual information, such as vehicular position information, is
a potential alternative to reduce such overheads. In this context, this paper
presents a deep learning-based solution on utilizing the vehicular position
information for predicting the optimal beams having sufficient mmWave received
powers so that the best V2V line-of-sight links can be ensured proactively.
After experimental evaluation of the proposed solution on real-world measured
mmWave sensing and communications datasets, the results show that the solution
can achieve up to 84.58% of received power of link status on average, which
confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V
communications.
