dorsal/arxiv
View SchemaAWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty
| Authors | Dror Jacoby, Yanzhi Li, Shuyue Yu, Nicola Di Cicco, Hagit Messer, Gil Zussman, Igor Kadota |
|---|---|
| Categories | |
| ArXiv ID | 2601.05978vv1 |
| URL | https://arxiv.org/abs/2601.05978 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
As emerging applications demand higher throughput and lower latencies, operators are increasingly deploying millimeter-wave (mmWave) links within x-haul transport networks, spanning fronthaul, midhaul, and backhaul segments. However, the inherent susceptibility of mmWave frequencies to weather-related attenuation, particularly rain fading, complicates the maintenance of stringent Quality of Service (QoS) requirements. This creates a critical challenge: making admission decisions under uncertainty regarding future network capacity. To address this, we develop a proactive slice admission control framework for mmWave x-haul networks subject to rain-induced fluctuations. Our objective is to improve network performance, ensure QoS, and optimize revenue, thereby surpassing the limitations of standard reactive approaches. The proposed framework integrates a deep learning predictor of future network conditions with a proactive Q-learning-based slice admission control mechanism. We validate our solution using real-world data from a mmWave x-haul deployment in a dense urban area, incorporating realistic models of link capacity attenuation and dynamic slice demands. Extensive evaluations demonstrate that our proactive solution achieves 2-3x higher long-term average revenue under dynamic link conditions, providing a scalable and resilient framework for adaptive admission control.
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"abstract": "As emerging applications demand higher throughput and lower latencies, operators are increasingly deploying millimeter-wave (mmWave) links within x-haul transport networks, spanning fronthaul, midhaul, and backhaul segments. However, the inherent susceptibility of mmWave frequencies to weather-related attenuation, particularly rain fading, complicates the maintenance of stringent Quality of Service (QoS) requirements. This creates a critical challenge: making admission decisions under uncertainty regarding future network capacity. To address this, we develop a proactive slice admission control framework for mmWave x-haul networks subject to rain-induced fluctuations. Our objective is to improve network performance, ensure QoS, and optimize revenue, thereby surpassing the limitations of standard reactive approaches. The proposed framework integrates a deep learning predictor of future network conditions with a proactive Q-learning-based slice admission control mechanism. We validate our solution using real-world data from a mmWave x-haul deployment in a dense urban area, incorporating realistic models of link capacity attenuation and dynamic slice demands. Extensive evaluations demonstrate that our proactive solution achieves 2-3x higher long-term average revenue under dynamic link conditions, providing a scalable and resilient framework for adaptive admission control.",
"arxiv_id": "2601.05978",
"authors": [
"Dror Jacoby",
"Yanzhi Li",
"Shuyue Yu",
"Nicola Di Cicco",
"Hagit Messer",
"Gil Zussman",
"Igor Kadota"
],
"categories": [
"cs.NI",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty",
"url": "https://arxiv.org/abs/2601.05978",
"version": "v1"
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