  Wireless backhauling at millimeter-wave frequencies (mmWave) in static
scenarios is a well-established practice in cellular networks. However, highly
directional and adaptive beamforming in today's mmWave systems have opened new
possibilities for self-backhauling. Tapping into this potential, 3GPP has
standardized Integrated Access and Backhaul (IAB) allowing the same base
station serve both access and backhaul traffic. Although much more
cost-effective and flexible, resource allocation and path selection in IAB
mmWave networks is a formidable task. To date, prior works have addressed this
challenge through a plethora of classic optimization and learning methods,
generally optimizing a Key Performance Indicator (KPI) such as throughput,
latency, and fairness, and little attention has been paid to the reliability of
the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB
mmWave networks. In addition to optimizing average performance, Safehaul
ensures reliability by minimizing the losses in the tail of the performance
distribution. We develop a novel simulator and show via extensive simulations
that Safehaul not only reduces the latency by up to 43.2% compared to the
benchmarks but also exhibits significantly more reliable performance (e.g.,
71.4% less variance in achieved latency).
