Millimeter-wave (mmWave) links are increasingly utilized in wireless x-haul transport to meet growing service demands. However, the inherent susceptibility of mmWave links to weather-related attenuation creates uncertainty about future network capacity which can significantly affect Quality of Service (QoS). This creates a critical challenge: how to make admission control decisions for slices with QoS requirements, balancing acceptance rewards against the risk of future QoS-violation penalties due to capacity uncertainty? To address this, we develop a proactive slice admission control framework that tightly integrates: (i) a predictor that leverages historical link measurements to forecast short-term attenuation and quantify uncertainty; and (ii) an admission control algorithm that incorporates both the predictions and uncertainties to maximize rewards and minimize QoS-violation penalties. We compare our framework against baseline, state-of-the-art, and idealized oracle algorithms using real-world mmWave x-haul data and residential traffic traces. Simulations suggest that our framework can achieve revenues that are 250% larger than baseline algorithms and 75% larger than state-of-the-art algorithms.

翻译：毫米波链路越来越多地应用于无线x-haul传输中，以满足日益增长的业务需求。然而，毫米波链路固有的对天气相关衰减的敏感性，造成了未来网络容量的不确定性，这可能显著影响服务质量。这带来了一个关键挑战：如何为具有QoS需求的切片做出准入控制决策，在接纳奖励与因容量不确定性导致的未来QoS违规惩罚风险之间取得平衡？为解决此问题，我们开发了一个主动切片准入控制框架，该框架紧密集成了：（i）一个预测器，利用历史链路测量数据来预测短期衰减并量化不确定性；以及（ii）一个准入控制算法，该算法结合预测结果和不确定性，以最大化奖励并最小化QoS违规惩罚。我们使用真实的毫米波x-haul数据和住宅流量轨迹，将我们的框架与基线算法、最先进算法以及理想化的先知算法进行了比较。仿真结果表明，我们的框架可以实现比基线算法高出250%、比最先进算法高出75%的收益。