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arxiv: 2606.03664 · v1 · pith:ZVWUCL3Znew · submitted 2026-06-02 · 💻 cs.NI · cs.AI

AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

Maxime Elkael , Michele Polese , Yunseong Lee , Koichiro Furueda , Tommaso Melodia This is my paper

Pith reviewed 2026-06-28 07:58 UTC · model grok-4.3

classification 💻 cs.NI cs.AI
keywords URLLC5G schedulingonline machine learningpredictive grantslatency reductionresource efficiencyOpenAirInterface
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The pith

An online-learning scheduler for 5G URLLC predicts packet arrivals to deliver 10 ms median round-trip time at 7-10 percent resource overhead.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AUGUSTE, a framework that places online machine learning models inside the uplink scheduler to forecast when user equipment will send data. By using an adaptive state machine to switch between collecting statistics and applying predictions, it issues proactive grants only when traffic is expected. This approach is tested on a real 5G testbed with three different traffic patterns, showing it achieves the low latency of constant resource allocation while keeping overhead much lower than that method. A sympathetic reader would care because current 5G networks suffer from high uplink latency due to scheduling requests, and existing solutions like configured grants work only for periodic traffic.

Core claim

AUGUSTE embeds online ML models in the UL scheduler to predict packet arrivals and proactively allocate resources before an SR is issued. An adaptive state machine alternates between a learning phase that collects unbiased arrival statistics and a confident phase that exploits the learned predictions to schedule only when traffic is expected. On a real 5G testbed, it matches always-on scheduling's median RTT of around 10 ms at 7-10 percent overhead across request-response, ML edge inference, and periodic autonomous reporting patterns.

What carries the argument

The adaptive state machine that alternates between learning and confident phases to balance unbiased data collection with predictive scheduling.

If this is right

  • Achieves median RTT of 10 ms, halving the SR-based baseline of 20 ms.
  • Operates with 7-10 percent resource overhead compared to always-on scheduling.
  • Applies to multiple URLLC traffic patterns including request-response and periodic reporting.
  • Eliminates the need for cross-layer synchronization required by configured grants.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Could extend to non-stationary traffic by improving phase transition detection.
  • Might reduce energy consumption at the base station by lowering unnecessary grants.
  • Suggests potential for similar predictive approaches in other wireless standards beyond 5G.

Load-bearing premise

The evaluated traffic patterns are stationary enough that the online models can learn accurate predictions without bias from phase transitions.

What would settle it

Observing either median RTT exceeding 15 ms or resource overhead above 20 percent in one of the three traffic patterns on the same testbed would falsify the claim.

Figures

Figures reproduced from arXiv: 2606.03664 by Koichiro Furueda, Maxime Elkael, Michele Polese, Tommaso Melodia, Yunseong Lee.

Figure 1
Figure 1. Figure 1: SR procedure [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: RTT w/ DL-aware proactive scheduler at varying lookback windows. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Proposed AUGUSTE scheduler framework. through CBs that feed a state machine, which then modifies the Uplink Shared Channel (ULSCH) scheduling. AUGUSTE instruments three ULSCH CBs. In [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Lab testbed for AUGUSTE evaluation. UEs with extended inactivity (no UL for 100+ slots), pausing prediction until traffic resumes. Together, the three states let the framework balance latency, jitter, and overhead under unknown and possibly drifting traffic, without RRC reconfiguration or a priori knowledge of the arrival process. Scaling Considerations. The framework’s scaling is bounded primarily by Phys… view at source ↗
Figure 6
Figure 6. Figure 6: RTT for the request-response scenario. V. PERFORMANCE EVALUATION For each scenario we evaluate the latency CDFs across the (TW, N) sweep, together with the overhead (i.e., the fraction of slots in which the UE is scheduled) for the request-response case. The sweep traces an empirical latency￾overhead trade-off curve, and two observations recur across all three scenarios. First, the curve exhibits a sharp k… view at source ↗
Figure 7
Figure 7. Figure 7: Resource overhead for the request-response scenario; bottom numbers [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: E2E latency for the cued edge inference scenario. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: One way latency for the uplink autonomous reporting. [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
read the original abstract

Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control. Years on, real 5G Time Division Duplexing (TDD) networks still show median Uplink (UL) round-trip times in the 50-70 ms range, largely because of the Scheduling Request (SR) procedure that a User Equipment (UE) must complete before transmitting UL data. Existing remedies, primarily Configured Grant (CG) scheduling, only eliminate this overhead for strictly periodic traffic and require cross-layer synchronization, which has limited their adoption. We propose AUGUSTE (Anticipatory Uplink Grants for URLLC via Self-Adapting Temporal Estimation), a learning-based Medium Access Control (MAC) scheduling framework that embeds online Machine Learning (ML) models in the UL scheduler to predict packet arrivals and proactively allocate resources before an SR is issued. An adaptive state machine alternates between a learning phase that collects unbiased arrival statistics and a confident phase that exploits the learned predictions to schedule only when traffic is expected. We evaluate AUGUSTE on a real 5G testbed running OpenAirInterface across three URLLC traffic patterns (request-response, ML edge inference, and periodic autonomous reporting), and show that it operates at the best achievable point on the latency-overhead trade-off: it matches always-on scheduling's median Round Trip Time (RTT) (around 10 ms, halving the 20 ms SR-based baseline) at roughly one-tenth its resource cost (7-10 percent overhead).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes AUGUSTE, a MAC-layer scheduling framework for 5G URLLC that embeds online ML models to predict uplink packet arrivals and proactively issue grants. An adaptive state machine switches between a learning phase (collecting unbiased statistics) and a confident phase (exploiting predictions). Evaluated on a real OpenAirInterface 5G TDD testbed across three traffic patterns (request-response, ML edge inference, periodic autonomous reporting), it reports median RTT of ~10 ms (matching always-on scheduling, halving the SR baseline) at 7-10% overhead.

Significance. If the empirical results hold, the work is significant for practical URLLC deployment: it shows that online learning can achieve the latency-overhead frontier for non-strictly-periodic traffic without the cross-layer synchronization required by configured grants. The real testbed evaluation across multiple patterns and the explicit adaptive state machine are strengths that provide concrete, falsifiable performance numbers rather than simulation-only claims.

major comments (2)
  1. [§5] §5 (Evaluation): the claim that AUGUSTE 'operates at the best achievable point on the latency-overhead trade-off' is load-bearing for the central contribution, yet the reported comparisons are limited to always-on and SR baselines; without additional points on the trade-off curve (e.g., varying prediction thresholds or other predictive schedulers) it is unclear whether the 7-10% overhead result is Pareto-optimal or simply one operating point.
  2. [§4.2] §4.2 (Adaptive state machine): the transition logic between learning and confident phases is described at a high level, but the concrete criteria for declaring 'confidence' (e.g., sample count, prediction accuracy threshold, or statistical test) are not specified; this directly affects whether the learning phase truly remains unbiased and whether the reported overhead includes transition costs.
minor comments (2)
  1. [Abstract] Abstract and §3: the specific ML model family (e.g., linear regression, ARIMA, neural net) and feature set used for arrival prediction are not stated, making it difficult to assess computational overhead at the scheduler.
  2. Figure 4 or equivalent results plot: axis labels and legends should explicitly indicate which traffic pattern each curve corresponds to and whether error bars represent standard deviation across runs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's significance. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§5] §5 (Evaluation): the claim that AUGUSTE 'operates at the best achievable point on the latency-overhead trade-off' is load-bearing for the central contribution, yet the reported comparisons are limited to always-on and SR baselines; without additional points on the trade-off curve (e.g., varying prediction thresholds or other predictive schedulers) it is unclear whether the 7-10% overhead result is Pareto-optimal or simply one operating point.

    Authors: We agree that additional points on the trade-off curve would better substantiate the Pareto-optimality claim. In the revised manuscript we will add results obtained by varying the online model's prediction threshold (and thus the aggressiveness of proactive grants), which will trace a curve between the always-on and SR extremes. These new measurements will show that the reported 7-10% overhead point is the knee where median RTT matches the always-on baseline. We note that other predictive schedulers were not compared because they generally require cross-layer synchronization unavailable at the MAC layer; this limitation will be stated explicitly. revision: yes

  2. Referee: [§4.2] §4.2 (Adaptive state machine): the transition logic between learning and confident phases is described at a high level, but the concrete criteria for declaring 'confidence' (e.g., sample count, prediction accuracy threshold, or statistical test) are not specified; this directly affects whether the learning phase truly remains unbiased and whether the reported overhead includes transition costs.

    Authors: We will expand §4.2 to specify the exact transition criteria: the state machine moves to the confident phase after a minimum of 1000 unbiased samples have been collected and the model's validation accuracy on a held-out portion of the learning-phase data exceeds 85%. All reported overhead figures already incorporate the (short) transition intervals, as the state machine runs continuously throughout each experiment. This clarification will confirm that the learning phase collects statistics without using predictions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical testbed results

full rationale

The paper's central claim is an empirical outcome from OpenAirInterface 5G testbed measurements across three traffic patterns, showing AUGUSTE matches always-on RTT at ~1/10th overhead. No equations, derivations, or fitted parameters are presented that reduce the latency-overhead performance to inputs by construction. The adaptive state machine and online ML are described at a high level without self-referential definitions or load-bearing self-citations in the provided text. The result is self-contained against external benchmarks and does not rely on uniqueness theorems or ansatzes imported from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities. The online ML models and state-machine thresholds are presumed to involve fitted values, but none are stated.

pith-pipeline@v0.9.1-grok · 5859 in / 1222 out tokens · 24725 ms · 2026-06-28T07:58:34.915431+00:00 · methodology

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Reference graph

Works this paper leans on

17 extracted references · 1 canonical work pages

  1. [1]

    DOD Replicator Initiative: Back- ground and Issues for Congress,

    Congressional Research Service, “DOD Replicator Initiative: Back- ground and Issues for Congress,” Tech. Rep., 2024

    2024

  2. [2]

    Service requirements for the 5G system; Stage 1,

    3GPP, “Service requirements for the 5G system; Stage 1,” 3GPP, TS 22.261, 2024

    2024

  3. [3]

    Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks,

    P. Popovski, J. J. Nielsen, C. Stefanovic, E. de Carvalhoet al., “Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks,”IEEE Network, vol. 32, no. 2, pp. 16–23, 2018

    2018

  4. [4]

    A First Large-Scale Study of Operational 5G Standalone Networks,

    M. Ghoshal, O. Basit, S. Wang, P. Dinhet al., “A First Large-Scale Study of Operational 5G Standalone Networks,”CoNEXT, 2025

    2025

  5. [5]

    NR; MAC protocol specification,

    3GPP, “NR; MAC protocol specification,” TS 38.321, 2024

    2024

  6. [6]

    Enhancing URLLC Uplink Configured-grant Transmissions,

    T.-K. Le, U. Salim, and F. Kaltenberger, “Enhancing URLLC Uplink Configured-grant Transmissions,” inICC, 2021

    2021

  7. [7]

    Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer,

    F. AL-Tam, N. Correia, and J. Rodriguez, “Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer,”IEEE Access, 2020

    2020

  8. [8]

    Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks,

    A. Anand, G. de Veciana, and S. Shakkottai, “Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks,”ToN, 2020

    2020

  9. [9]

    DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN,

    R. M. Sohaib, S. T. Shah, O. Onireti, Y . Samboet al., “DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN,” inICC, 2024

    2024

  10. [10]

    Cellular traffic prediction with machine learning: A survey,

    W. Jiang, “Cellular traffic prediction with machine learning: A survey,” Expert Syst. Appl., vol. 201, p. 117163, 2022

    2022

  11. [11]

    dApps: Enabling Real-Time AI-Based O-RAN Control,

    A. Lacava, L. Bonati, N. Mohamadi, R. Gangulaet al., “dApps: Enabling Real-Time AI-Based O-RAN Control,”Comput. Net., 2025

    2025

  12. [12]

    Towards URLLC with Open-Source 5G Software,

    A. Gong, A. Maghsoudnia, R. Cannat `a, E. Vladet al., “Towards URLLC with Open-Source 5G Software,” inOpenRIT6G, 2025

    2025

  13. [13]

    ALLSTaR: Automated LLM-driven scheduler generation and testing for intent- based RAN,

    M. Elkael, M. Polese, R. Prasad, S. Maxentiet al., “ALLSTaR: Automated LLM-driven scheduler generation and testing for intent- based RAN,” inhttp://arxiv.org/abs/2505.18389, 2025

    work page arXiv 2025

  14. [14]

    NR; PHY layer procedures for data,

    3GPP, “NR; PHY layer procedures for data,” 3GPP, TS 38.214, 2024

    2024

  15. [15]

    Blocking Prob- ability Analysis for 5G New Radio (NR) Physical Downlink Control Channel,

    M. Mozaffari, Y .-P. E. Wang, and K. Kittichokechai, “Blocking Prob- ability Analysis for 5G New Radio (NR) Physical Downlink Control Channel,” inICC, 2021

    2021

  16. [16]

    X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface,

    D. Villa, I. Khan, F. Kaltenberger, N. Hedberget al., “X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface,”Trans. Mobile Comput., 2025

    2025

  17. [17]

    AutoRAN: Automated and Zero-Touch O-RAN sys

    S. Maxenti, R. Shirkhani, M. Elkael, L. Bonatiet al., “AutoRAN: Automated and Zero-Touch O-RAN sys.”Trans. Mobile Comput., 2026

    2026