# Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction

arXiv: https://arxiv.org/abs/2302.07675 · HTML: https://mlsanity.com/paper/2302.07675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 25 of 25)

- [Adaptive Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2106.00170/) (2021)
- [Conformal prediction beyond exchangeability](https://mlsanity.com/paper/2202.13415/) (2022)
- [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019)
- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [Safe Planning in Dynamic Environments using Conformal Prediction](https://mlsanity.com/paper/2210.10254/) (2022)
- [Fair Conformal Predictors for Applications in Medical Imaging](https://mlsanity.com/paper/2109.04392/) (2021)
- [Achieving Risk Control in Online Learning Settings](https://mlsanity.com/paper/2205.09095/) (2022)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View](https://mlsanity.com/paper/1804.05057/) (2018)
- [Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction](https://mlsanity.com/paper/2210.03067/) (2022)
- [Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/2003.07651/) (2020)
- [Conformal Prediction for Trustworthy Detection of Railway Signals](https://mlsanity.com/paper/2301.11136/) (2023)
- [Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks](https://mlsanity.com/paper/1712.05344/) (2017)
- [Three Applications of Conformal Prediction for Rating Breast Density in Mammography](https://mlsanity.com/paper/2206.12008/) (2022)
- [eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach](https://mlsanity.com/paper/1902.01648/) (2019)
- [Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networks](https://mlsanity.com/paper/1804.09201/) (2018)
- [Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC](https://mlsanity.com/paper/1905.11017/) (2019)
- [Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction](https://mlsanity.com/paper/2210.04621/) (2022)
- [Wireless Channel Modeling Perspectives for Ultra-Reliable Low Latency Communications](https://mlsanity.com/paper/1705.01725/) (2017)
- [Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach](https://mlsanity.com/paper/1912.03359/) (2019)
- [A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks](https://mlsanity.com/paper/2009.09953/) (2020)
- [Coexistence of URLLC and eMBB services in the C-RAN Uplink: An Information-Theoretic Study](https://mlsanity.com/paper/1804.06593/) (2018)
- [Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures](https://mlsanity.com/paper/1809.01399/) (2018)
- [Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation](https://mlsanity.com/paper/2003.05685/) (2020)
- [A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios](https://mlsanity.com/paper/2111.15630/) (2021)

## Built on by (most cited first, 6 of 6)

- [Conformal Prediction Regions for Time Series using Linear Complementarity Programming](https://mlsanity.com/paper/2304.01075/) (2023)
- [Cross-Validation Conformal Risk Control](https://mlsanity.com/paper/2401.11974/) (2024)
- [Mirror Online Conformal Prediction with Intermittent Feedback](https://mlsanity.com/paper/2503.10345/) (2025)
- [Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints](https://mlsanity.com/paper/2409.07902/) (2024)
- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms](https://mlsanity.com/paper/2610.17962/) (2026)

## Abstract (arXiv)

The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this paper, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances in online conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.