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arxiv: 2511.21277 · v3 · pith:ABLR2L5Jnew · submitted 2025-11-26 · 💻 cs.NI

LatencyScope: A System-Level Mathematical Framework for 5G RAN Latency

Arman Maghsoudnia , Aoyu Gong , Raphael Cannat\`a , Dan Mihai Dumitriu , Haitham Hassanieh This is my paper

Pith reviewed 2026-05-21 19:22 UTC · model grok-4.3

classification 💻 cs.NI
keywords 5G RANlatency modelingmathematical frameworkprotocol stackURLLCconfiguration optimizationnetwork simulatorstestbed validation
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The pith

LatencyScope computes accurate 5G RAN latencies by modeling all major delay sources across the protocol stack and their dependencies.

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

This paper introduces LatencyScope as a system-level mathematical framework for calculating one-way uplink and downlink latencies in 5G radio access networks. It models key delay sources including radio interfaces, scheduling, processing delays, frame structures, and hardware and software constraints, while accounting for parameter dependencies and stochastic variations. The framework also features a configuration analyzer that searches through billions of possible settings to find those meeting specified latency and reliability targets under given constraints. Validation on open-source testbeds and a commercial network shows close agreement with empirical latency distributions and bounds, while outperforming existing analytical models and simulators. A sympathetic reader would care because this enables network operators to evaluate the feasibility of ultra-reliable low-latency communication and optimize deployments accordingly.

Core claim

LatencyScope models latency sources across the 5G protocol stack, including radio interfaces, scheduling decisions, processing delays, frame structures, and hardware and software constraints, while capturing dependencies among configuration parameters and stochastic sources of delay. The framework computes one-way uplink and downlink latencies for diverse system configurations and includes a configuration analyzer that identifies settings satisfying latency-reliability targets. When validated on two open-source 5G RAN testbeds and measurements from a public commercial network, it closely matches empirical latency distributions, captures observed bounds, and outperforms prior models and simul

What carries the argument

LatencyScope, the mathematical model integrating latency contributions from across the protocol stack with their interdependencies and stochastic elements to enable precise latency calculation and configuration search.

Load-bearing premise

The modeled latency sources across the protocol stack plus their dependencies and stochastic elements are sufficient to reproduce real-world latency behavior without significant unmodeled effects.

What would settle it

A test where measured latency distributions in a 5G network fall outside the bounds predicted by LatencyScope or show systematic deviations not accounted for by the stochastic models in the framework.

Figures

Figures reproduced from arXiv: 2511.21277 by Aoyu Gong, Arman Maghsoudnia, Dan Mihai Dumitriu, Haitham Hassanieh, Raphael Cannat\`a.

Figure 1
Figure 1. Figure 1: Path of a Ping Request through the 5G Stack. in a 5G network, examining the network stacks of both the UE and the gNB [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the system-level latency for the journey of a packet. A TDD Common Configuration with the DDDU pattern is used. procedure noticeably increases the latency of UL transmis￾sions (cf. 2 5 ). An alternative grant-free access mechanism allocates resources to UEs without SRs, reducing latency but facing scalability issues as the number of UEs increases [10]. It is crucial to note the following points… view at source ↗
Figure 3
Figure 3. Figure 3: The 5G testbed setup. In addition to identifying the single best configuration under a chosen objective, the optimizer also supports find￾all-configurations that satisfy a given objective. In this case, the optimizer disables optimizations such as fixing parame￾ters with monotonically decreasing latency effects and the two-phase coarse–fine search, since these mechanisms are designed to eliminate non-optim… view at source ↗
Figure 4
Figure 4. Figure 4: Latency distributions for various scenarios. We generate synthetic traffic using different inter-arrival distributions and packet sizes: 1) Constant – Packets of 64 bytes with a fixed inter-arrival time of 101 ms. 2) Gaussian – Packets of 64 bytes with a Gaussian inter-arrival distribution (mean: 105 ms, standard deviation: 0.05). 3) Large Packets – Packets of 40 000 bytes with a fixed inter-arrival time o… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of latency bounds between LatencyScope and Real-World, where scenarios (a)–(h) are the same as in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of the latency distribution and Wasserstein distance between LatencyScope and the two baselines. 2 4 6 8 10 12 14 Packet Latency [ms] 0.0 0.2 0.4 0.6 0.8 1.0 CDF Real-World Latency￾Scope 5G-LENA MATLAB (a) Uplink, TDD (3D/1U), SR = 4 ms, 𝑘2 = 2, 𝑎1 = 3, Constant, srsRAN 4 6 8 10 12 14 16 18 20 22 Packet Latency [ms] 0.0 0.2 0.4 0.6 0.8 1.0 CDF Real-World Latency￾Scope 5G-LENA MATLAB (b) Uplink, … view at source ↗
Figure 7
Figure 7. Figure 7: Wasserstein distance for scenarios explained in [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of modelling 𝑙1 and 𝑝4 as random variables (right) versus constant values (left) distribution across two representative scenarios. In contrast, both MATLAB and 5G-LENA significantly underestimate the latency range and fail to replicate the actual distribution shape. Beyond accuracy, the computational cost of traditional simulators is prohibitive. For example, simulating the trans￾mission of 10,… view at source ↗
Figure 12
Figure 12. Figure 12: Example of how a shorter TDD pattern does not imply a lower latency. TDD pattern DDUU above, and DU below. 0.056 when we do not model the RVs and 0.027 when we do, which is a significant improvement. E. Configuration Optimizer: For the sub-6GHz band with Grant-Based configuration, as a representative case, the opti￾mizer explores a search space of 32 billion possible combina￾tions. The set of possible val… view at source ↗
Figure 13
Figure 13. Figure 13: B.2 Traffic Generation As introduced in §6, we developed a new traffic generator and replayer, more accurate than the Linux kernel’s built-in tools. We evaluate their performance on an Intel Xeon W￾2225 with 64GB RAM running Linux 6.8.0-50-lowlatency. Our tool consists of two components: a generator and a replayer. The generator produces packets with Gaussian, Poisson, or constant inter-arrival times, whi… view at source ↗
Figure 13
Figure 13. Figure 13: Latency distributions for additional scenarios. We generate traffic using different inter-arrival distributions and packet sizes: 1) Constant – Packets of 64 bytes with a fixed inter-arrival time of 101 ms. 2) Gaussian – Packets of 64 bytes with a Gaussian inter-arrival distribution (mean: 105 ms, standard deviation: 0.05). 40.0 40.2 40.4 40.6 40.8 41.0 [ms] 0.0 0.1 0.2 0.3 0.4 Frequency LatencyScope (mea… view at source ↗
Figure 14
Figure 14. Figure 14: Inter-arrival time of generated packets D.1 UL Latency for Size 2 packets in TDD Consider the case where a UE transmits a packet larger than the initial grant size, i.e., Eq. (28) no longer holds. The packet’s journey is shown in [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Journey of a packet for larger packets. A TDD Common Configuration with the DDUU pattern is used. 𝑤 ′ 5 : Time from the slot carrying the new grant to the uplink slot where the UE transmits using it. 𝑤 ′ 6 : Transmission time of data on granted uplink slots within a TDD period. Modeling of 𝑤 ′ 3 : As in § 4.1, the gNB requires 𝑝1 seconds to process UL samples, decoding symbol by symbol. Since scheduling s… view at source ↗
Figure 16
Figure 16. Figure 16: Journey of a packet for larger packets for the special case of extended grant latency. the multi-packet case, we model the per-byte evolution in the RLC buffer as a finite-state machine with the following states. • 𝑆0 (Arrived): The byte has just entered the buffer and has not yet been requested, reported, or granted. • 𝑆1 (Requested): The byte was present when the UE sent the scheduling request (SR). • 𝑆… view at source ↗
Figure 17
Figure 17. Figure 17: Finite-state machine of RLC-buffer byte states for a single user. Slot Downlink Data Uplink Data Pattern 1 Mixed Slot Symbol Mini Slot Mini Slot Downlink Control Uplink Control (a) (b) [PITH_FULL_IMAGE:figures/full_fig_p021_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of (a) Common Configuration and (b) Mini Slot in TDD 𝑢2 =    (𝑎1 + 1)𝑆, 𝑟𝑒1 ≤ 𝑑; (𝑎1 + 1 + (𝑇 − 𝑟𝑒1))𝑆, otherwise. (44) Modeling of 𝑢3: DL transmission time, proportional to the number of DL slots: 𝑢3 = 𝑁𝐷𝐿,𝑠𝑙𝑜𝑡𝑠 · 𝑆 (45) Modeling of 𝑢4: UE processing time after receiving DL data: 𝑢4 = 𝑙3 (46) Modeling the rest of the transmissions: If one TDD pe￾riod is insufficient, the process mirrors… view at source ↗
read the original abstract

This paper presents LatencyScope, a mathematical framework for computing one-way uplink and downlink latency in fifth-generation radio access networks across diverse system configurations. LatencyScope models latency sources across the protocol stack, including radio interfaces, scheduling decisions, processing delays, frame structures, and hardware and software constraints, while capturing dependencies among configuration parameters and stochastic sources of delay. The framework also includes a configuration analyzer that uses these models to search billions of candidate settings and identify those that satisfy latency-reliability targets under user-specified constraints. We validate LatencyScope on two open-source fifth-generation radio access network testbeds, as well as on measurements from a public commercial fifth-generation network. The results show that LatencyScope closely matches empirical latency distributions, captures observed lower and upper latency bounds, and substantially outperforms prior analytical models and widely used fifth-generation network simulators. LatencyScope can determine whether ultra-reliable low-latency communication targets are feasible for a given deployment and, when they are feasible, efficiently find satisfying configurations, helping network operators reason about latency modeling, configuration analysis, and system-level bottlenecks.

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 paper presents LatencyScope, a mathematical framework for computing one-way uplink and downlink latency in 5G radio access networks. It models latency sources including radio interfaces, scheduling, processing delays, frame structures, and hardware/software constraints, along with their dependencies and stochastic elements. The framework includes a configuration analyzer to search for settings satisfying latency-reliability targets. Validation on two open-source 5G RAN testbeds and commercial network measurements shows close matches to empirical latency distributions, capture of bounds, and outperformance over prior analytical models and simulators.

Significance. If the validation demonstrates that the modeled latency sources are sufficient and parameters are not tuned to the validation data, LatencyScope could be a significant contribution for network operators and researchers in assessing and optimizing 5G configurations for ultra-reliable low-latency communication (URLLC) targets. It provides a system-level approach that goes beyond component-wise analysis and could reduce reliance on full-scale simulations.

major comments (2)
  1. [Validation results] Validation section: The central claim that LatencyScope closely matches empirical latency distributions, captures observed bounds, and substantially outperforms prior models rests on comparisons to measurements from two open-source testbeds and a commercial network. The manuscript does not clarify whether parameters (e.g., processing delay distributions, scheduling probabilities, or hardware constraints) are derived exclusively from 3GPP specifications and first principles or adjusted using the same empirical data. If any calibration to validation measurements occurred, the reported match would be consistent with in-sample fitting rather than an independent test of whether unmodeled effects are negligible.
  2. [Mathematical framework] Model formulation: The framework claims to capture dependencies among configuration parameters and stochastic delay sources. The description does not specify the exact mathematical structure used to combine these (e.g., whether total latency is expressed as a sum of independent random variables, a joint distribution, or a queueing model with explicit conditioning), which is load-bearing for the claimed ability to reproduce lower and upper latency bounds.
minor comments (2)
  1. [Abstract] The abstract states outperformance versus 'prior analytical models and widely used fifth-generation network simulators' without naming the specific baselines (e.g., which simulators or which prior latency models). Adding these names would improve context.
  2. [Throughout] Notation for latency components and configuration parameters should be introduced once with a clear table or list of symbols to avoid ambiguity when the analyzer searches billions of candidate settings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below with clarifications and note revisions that will be incorporated in the revised manuscript.

read point-by-point responses

  1. Referee: [Validation results] Validation section: The central claim that LatencyScope closely matches empirical latency distributions, captures observed bounds, and substantially outperforms prior models rests on comparisons to measurements from two open-source testbeds and a commercial network. The manuscript does not clarify whether parameters (e.g., processing delay distributions, scheduling probabilities, or hardware constraints) are derived exclusively from 3GPP specifications and first principles or adjusted using the same empirical data. If any calibration to validation measurements occurred, the reported match would be consistent with in-sample fitting rather than an independent test of whether unmodeled effects are negligible.

    Authors: We agree that explicit clarification on parameter sources is necessary to establish the validation as an independent test. All parameters in LatencyScope—including processing delay distributions, scheduling probabilities, and hardware constraints—are derived exclusively from 3GPP specifications, publicly available vendor documentation, and first-principles derivations. No parameters were fitted or calibrated to the validation measurements from the testbeds or commercial network; those data were used only for post-specification comparison. We will revise the validation section to add a dedicated paragraph (and table) listing the source of every parameter and confirming the absence of any tuning to the reported datasets. revision: yes

  2. Referee: [Mathematical framework] Model formulation: The framework claims to capture dependencies among configuration parameters and stochastic delay sources. The description does not specify the exact mathematical structure used to combine these (e.g., whether total latency is expressed as a sum of independent random variables, a joint distribution, or a queueing model with explicit conditioning), which is load-bearing for the claimed ability to reproduce lower and upper latency bounds.

    Authors: We acknowledge that the precise composition method must be stated explicitly. LatencyScope expresses one-way latency as the sum of random variables for each delay source, with dependencies among configuration parameters and stochastic elements captured via conditional distributions and joint probability models (rather than assuming full independence or using a classical queueing formulation). Lower and upper bounds follow directly from the support of the resulting distribution after convolution of independent components and conditioning on dependent ones. We will expand the model formulation section with the explicit mathematical definitions, including the conditioning structure and how bounds are obtained. revision: yes

Circularity Check

0 steps flagged

No significant circularity in LatencyScope derivation chain

full rationale

The paper constructs LatencyScope from explicit models of radio interfaces, scheduling, processing delays, frame structures, hardware/software constraints and their stochastic dependencies, drawing on protocol specifications. The configuration analyzer then searches over these models to identify feasible settings. Validation against separate open-source testbed and commercial network measurements is presented as an independent check rather than a fitted input. No equations, self-citations, or steps are quoted that reduce a claimed prediction or first-principles result to the validation data by construction, nor is any uniqueness theorem or ansatz smuggled in via prior self-work. The central claim therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework relies on standard mathematical modeling of delays and stochastic processes; no free parameters, ad-hoc axioms, or invented entities are explicitly named in the abstract, though the modeling of dependencies and stochastic sources implicitly assumes that all major latency contributors are captured by the chosen equations.

axioms (1)
  • domain assumption Latency contributions from radio interfaces, scheduling, processing, frame structures, and hardware/software constraints can be expressed as interdependent mathematical expressions that include stochastic terms.
    Invoked throughout the description of the models in the abstract.

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