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

arXiv: https://arxiv.org/abs/2601.05978 · HTML: https://mlsanity.com/paper/2601.05978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://mlsanity.com/paper/2303.06053/) (2023)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2111.01616/) (2021)
- [Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks](https://mlsanity.com/paper/1902.09696/) (2019)
- [Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks](https://mlsanity.com/paper/1802.04491/) (2018)
- [Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration](https://mlsanity.com/paper/2203.03383/) (2022)
- [Admission Control and Resource Reservation for Prioritized Slice Requests with Guaranteed SLA under Uncertainties](https://mlsanity.com/paper/2203.09367/) (2022)
- [Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage](https://mlsanity.com/paper/2205.09436/) (2022)
- [Atlas: Automate Online Service Configuration in Network Slicing](https://mlsanity.com/paper/2210.16902/) (2022)
- [Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks](https://mlsanity.com/paper/2301.03201/) (2023)
- [AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks](https://mlsanity.com/paper/2501.06943/) (2025)

## Abstract (arXiv)

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.