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

Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Petar Popovski, Shlomo Shamai

TL;DR

The paper tackles dynamic uplink URLLC scheduling under uncertain traffic predictors. It introduces an online conformal-prediction–based scheduler that guarantees reliability $1-\alpha$ and latency irrespective of predictor quality by adaptively controlling the number of URLLC slots allocated using a Gamma-based pattern set and a stretching function. The approach yields formal reliability guarantees and improves eMBB efficiency by trading off over-provisioning required by naive predictors. The results demonstrate robust performance across predictor mismatches and highlight practical impact for coexisting URLLC and eMBB services.

Abstract

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.

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

TL;DR

The paper tackles dynamic uplink URLLC scheduling under uncertain traffic predictors. It introduces an online conformal-prediction–based scheduler that guarantees reliability and latency irrespective of predictor quality by adaptively controlling the number of URLLC slots allocated using a Gamma-based pattern set and a stretching function. The approach yields formal reliability guarantees and improves eMBB efficiency by trading off over-provisioning required by naive predictors. The results demonstrate robust performance across predictor mismatches and highlight practical impact for coexisting URLLC and eMBB services.

Abstract

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.
Paper Structure (6 sections, 8 equations, 3 figures, 2 algorithms)

This paper contains 6 sections, 8 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Definition
  3. CP-Based URLLC Resource Allocation
  4. Naïve Prediction-Based Scheduler
  5. CP-Based Scheduler
  6. Experiments and Conclusions

Figures (3)

  • Figure 1: (a) Data packet generation for URLLC traffic across successive frames (URLLC packets are shown in the darker color). This information is unavailable at the scheduler, which has access only to a predictor that may underestimate or overestimate the number of URLLC packets to be generated (as in parts (b) and (c) respectively). (b) In the former case, a conventional resource allocation scheme that trusts the predictor fails to reliably serve URLLC data (slots allocated for URLLC are in darker color), resulting in an average frame success ratio of $82\%$ that falls short of the target of $90\%$ (for illustrative purposes we set the target unreliability rate to be modest using $\alpha=0.1$, our numerical part uses a tighter value). Scheduling error are shown as darker slots in the sidebar. (c) With an overestimating predictor, a conventional scheduler allocates excessive resources to URLLC traffic, severely impairing eMBB efficiency. eMBB traffic can occupy all slots unassigned to URLLC packets. In either case, the proposed CP-based scheduler is able to meet the URLLC reliability target of $90\%$ by properly adjusting the eMBB spectral efficiency.
  • Figure 2: (a) The assumed frame-based communication: each frame $f$ contains $S$ slots that can be allocated for either eMBB or URLLC traffic. (b) Illustration of the generation of $G_f=4$ URLLC packets, with each packet generated at a slot marked by an upward arrow incoming into the frame. Each URLLC packet must find an available slot within a maximum delay of $L=2$ slots in order to meet latency requirements. With the given resource allocation, the first three packets are transmitted in the corresponding slots indicated with an upward outgoing arrow, while the fourth packet does not find any available slot within the delay constraint. (c) For the illustrated distinct slot allocation, all URLLC packets are transmitted within the allowed latency of $L=2$ slots.
  • Figure 3: URLLC reliability rate \ref{['eq: reliability empirical']} and eMBB efficiency \ref{['eq:eMBB_efficiency']} for conventional scheduler (Sec. \ref{['sec: Naive Prediction-Based Scheduler']}) and CP-based scheduler (Sec. \ref{['sec: CP-Based Scheduler']}) as a function of the ground-truth traffic parameter and predictor parameter. The target rate is $1-\alpha=0.99$ (dashed red line for conventional scheduler; the CP-based scheduler always satisfies the reliability condition).