Safehaul：风险规避学习在6G网络中实现可靠的mmWave自回程
Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks
摘要：
在静态场景中以毫米波频率（mmWave）的无线回程是蜂窝网络中的成熟实践。然而，当今毫米波系统中的高度定向和自适应波束成形为自回程开辟了新的可能性。利用这种潜力，3GPP已经标准化了集成接入和回程（IAB），允许同一基站服务接入和回程业务。尽管IAB毫米波网络更具成本效益和灵活性，但资源分配和路径选择是一项艰巨的任务。迄今为止，现有的工作已经通过大量的经典优化和学习方法来解决这个挑战，通常优化关键性能指标（KPI），例如吞吐量、延迟和公平性，并且很少关注KPI的可靠性。我们提出了Safehaul，一种针对IAB毫米波网络的基于风险规避学习的解决方案。除了优化平均性能外，Safehaul还通过最大限度地减少性能分布尾部的损失来确保可靠性。我们开发了一种新的模拟器，并通过大量的模拟表明，Safehaul不仅将延迟降低了43.2%，而且还表现出更可靠的性能（例如，实现延迟的方差减少71.4%）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
