针对毫米波链路天气衰减不确定性，提出主动切片准入控制框架。
AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty
- 结合历史数据预测短时衰减与不确定度，实现前瞻性决策。
- 相比基线算法收益提升250%，较先进算法提升75%。
- 适合需要高可靠无线回传的5G/6G网络运营商使用。
毫米波（mmWave）链路在无线x-haul传输中日益普及，以满足不断增长的服务需求。然而，毫米波链路对天气引起的衰减高度敏感，导致未来网络容量存在不确定性，显著影响服务质量（QoS）。这带来关键挑战：如何在满足切片QoS要求的前提下，权衡接纳收益与因容量不确定性引发的未来QoS违规惩罚？为此，我们开发了一种主动切片准入控制框架，紧密集成：(i) 利用历史链路测量数据预测短期衰减并量化不确定性；(ii) 融合预测与不确定性的准入控制算法，以最大化收益并最小化QoS违规惩罚。基于真实毫米波x-haul数据和住宅流量痕迹，我们将该框架与基线、先进及理想化基准算法进行对比。仿真结果表明，该框架可使收益达到基线算法的2.5倍，较先进算法高出75%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。