mlsanityData to 14 Oct 2026

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

arXiv 2302.07675 ↗First posted 15 Feb 2023eess.SP cs.LG cs.NIcited by 6

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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.

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

25 of 40 references.

References · 25

  1. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  2. 2022Conformal prediction beyond exchangeabilitycited by 355
  3. 2019Predictive inference with the jackknife+cited by 211
  4. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  5. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 119
  6. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  7. 2022Achieving Risk Control in Online Learning Settingscited by 39
  8. 2020DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detectioncited by 24
  9. 20185G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic Viewcited by 18
  10. 2022Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Predictioncited by 17
  11. 2020Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approachcited by 10
  12. 2023Conformal Prediction for Trustworthy Detection of Railway Signalscited by 7
13 more
  1. 2017Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networkscited by 6
  2. 2022Three Applications of Conformal Prediction for Rating Breast Density in Mammographycited by 6
  3. 2019eMBB-URLLC Resource Slicing: A Risk-Sensitive Approachcited by 5
  4. 2018Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networkscited by 4
  5. 2019Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLCcited by 4
  6. 2022Calibrating AI Models for Few-Shot Demodulation via Conformal Predictioncited by 4
  7. 2017Wireless Channel Modeling Perspectives for Ultra-Reliable Low Latency Communicationscited by 2
  8. 2019Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approachcited by 2
  9. 2020A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networkscited by 2
  10. 2018Coexistence of URLLC and eMBB services in the C-RAN Uplink: An Information-Theoretic Studycited by 1
  11. 2018Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architecturescited by 1
  12. 2020Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocationcited by 1
  13. 2021A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarioscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.

6 papers.