Gargari, Amir Ashtari ; Ortiz Jimenez, Andrea Patricia ; Pagin, Matteo ; Klein, Anja ; Hollick, Matthias ; Zorzi, Michele ; Asadi, Arash (2023)
Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks.
42nd IEEE Conference on Computer Communications (INFOCOM 2023). New York City, USA (17.05.2023 - 20.05.2023)
doi: 10.1109/INFOCOM53939.2023.10228969
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Kurzbeschreibung (Abstract)

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 to 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.

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