AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge of slice admission control in millimeter-wave (mmWave) x-haul networks, where rain-induced link capacity fluctuations hinder the simultaneous satisfaction of stringent QoS requirements and long-term revenue objectives. To this end, the paper proposes a proactive admission control framework that uniquely integrates deep learning–based link capacity prediction with a Q-learning–optimized dynamic decision mechanism. By jointly forecasting future network states under capacity uncertainty and preemptively adjusting admission policies, the framework enables anticipatory resource allocation. Extensive experiments based on real-world urban mmWave deployment data demonstrate that the proposed approach achieves a 2–3× improvement in long-term average revenue compared to conventional reactive schemes, while maintaining strong scalability and robustness.

Technology Category

• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

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.

Problem

• mmWave
• slice admission control
• weather-induced attenuation
• capacity uncertainty
• QoS

Innovation

• proactive slice admission control
• mmWave x-haul networks
• weather-induced capacity uncertainty
• deep learning prediction
• Q-learning