# Decomposing Delay in 5G: An Empirical Study on Architecture and Configuration Impact

MohammadBagher Tavassoli, Chadi Barakat, Thierry Turletti, Walid Dabbous

Centre Inria d'Université Côte d'Azur, Sophia Antipolis, France

{mohammadbagher.tavassoli-kejani, chadi.barakat, thierry.turletti, walid.dabbous}@inria.fr

***Abstract***—5G networks have the potential for ultra-low latency communication, supporting a broad array of critical and delay-sensitive applications. However, end-to-end performance prediction in 5G scenarios is still a challenging problem, given the complexity of the system architecture and the interconnection of a multitude of interrelated parameters that contribute to delay. In this work, we report an experiment-driven investigation on the basis of an actual standalone 5G deployment to better understand how interdependent configuration parameters, such as TDD configuration and scheduling request (SR) settings, contribute to round-trip time (RTT). For instance, in a 2 ms TDD configuration, RTT was reduced by 30% after aligning SR periodicity with the frame structure, highlighting the decisive impact of scheduling configuration. Through the integration of fine-grained measurement and delay component analysis, we reveal the major contributors to latency and highlight the crucial role of empirical assessment and careful parameter tuning in realizing dependable low-latency operation in 5G networks.

***Index Terms***—5G latency, delay decomposition, experimental evaluation, openAirInterface

## I. INTRODUCTION

5G networks are designed to support a broad range of services across diverse application domains, ranging from enhanced mobile broadband (eMBB) to massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC). These services are associated with vastly different requirements in terms of throughput, reliability, and latency. Among them, URLLC use cases such as autonomous vehicles, teleoperation, and factory automation pose the strictest delay constraints, often targeting one-way delays in the order of 1 ms or less [1], [2]. To support such demands, 5G incorporates several enhancements in the radio access network, including reduced signaling overhead, short-range communications, adaptable frame structures, and scheduling mechanisms optimized for low-latency transmission. In parallel, architectural innovations such as software-defined networking (SDN), network function virtualization (NFV), and multi-access edge computing (MEC) have been introduced to improve programmability and service flexibility across the radio access, transport, and core segments of the network [3].

However, this architectural flexibility comes at a cost. As 5G systems become more configurable and distributed, understanding and managing their delay behavior becomes increasingly complex. Unlike earlier mobile generations, which

offered limited flexibility in configuration and scheduling, 5G exposes a broad range of tunable parameters and deployment options. While theoretical analyses and simulations can provide delay estimates under ideal conditions, they often do not explain the variability seen in actual systems. Real-world deployments involve implementation-specific details, cross-layer interactions, and hardware dependencies that are difficult to capture analytically. As a result, practical and system-level insights are essential to fully understand how 5G networks behave in practice.

This paper focuses on dissecting the delay components in 5G networks through architectural analysis and experimental evaluation with particular emphasis on the Radio Access Network (RAN). By deploying a standalone 5G system using OpenAirInterface (OAI) [4] and measuring delay metrics under varying transmission and scheduling configurations, we aim to understand how specific network parameters can influence the overall delay experienced by applications at upper layers.

## II. RELATED WORKS

Delay in 5G networks has been studied from several perspectives, including architectural design, empirical measurement, predictive modeling, and cross-layer instrumentation. Popovski et al. [3] provided an early framework for supporting ultra-reliable low-latency communication (URLLC), outlining key mechanisms such as lean signaling, mini-slot transmission, and flexible frame structures. While foundational, their work remains theoretical and focuses on PHY/MAC-level capabilities rather than full system behavior. In parallel, the 3GPP introduced the Network Data Analytics Function (NWDAF) [5] as part of its 5G system architecture. NWDAF enables analytics-driven network control by exposing delay and performance metrics across domains. However, it relies on abstracted KPIs such as mean RAN latency, end-to-end throughput, or packet loss, and operator-driven data collection, without offering insights into the internal timing behavior of protocol layers or user-plane components. Fezeu et al. [6] offer one of the first large-scale measurement studies of commercial 5G deployments. Their work highlights uplink scheduler behavior and retransmission patterns as key sources of latency. The scope, however, is limited to external RTT (Round-Trip Time) metrics, without visibility into protocol-layer contributions. In contrast, LatSeq by Ronteix-Jacquet et al. [7] focuses on internal timing analysis within the OAI stack. LatSeq traces latency across software layers of the eNB, offering detailed insight

<footnote>This work was supported by the ANR under the France 2030 program, grant "NF-NAI: ANR-22-PEFT-0003".</footnote>
into RAN-side processing. Still, it does not cover the UE (User Equipment), core network, or overall end-to-end delay. Mostafavi et al. [8] extend this direction by proposing EDAF, a cross-layer analytics framework capable of timestamping at multiple protocol boundaries across the RAN, core, and application layers. EDAF enables precise delay decomposition and has shown how queuing, radio scheduling, and processing time shape uplink delay. However, it primarily evaluates fixed test scenarios and does not explore how different architectural configurations or traffic dynamics influence delay composition. Rao et al. [9] propose exposing predicted delay metrics at the base station, enabling both real-time notifications when thresholds are exceeded and facilitating automated network management actions based on these predictions.

Together, these works reflect steady progress in understanding 5G delay behavior from abstract models to protocol-level tracing. However, they lack visibility into implementation specific issues, motivating the need for our empirical study. Our work builds on this foundation by analyzing how configurable and architectural parameters affect delay in a complete 5G testbed. Using OpenAirInterface (OAI) [4] and system-level instrumentation, we isolate and quantify delay components under varying conditions and system settings, offering insights grounded in realistic deployment behavior.

### III. SYSTEM MODEL AND BACKGROUND

Understanding the origins of different parts of the delay in a standalone 5G network requires a close look at both the radio access procedures and key system parameters that shape packet scheduling and transmission timelines. A central feature of 5G New Radio (NR) is its flexible frame structure and support for Time Division Duplexing (TDD), which enables dynamic partitioning of uplink (UL) and downlink (DL) resources within each radio frame [12]. The configuration of these frames, specifically the arrangement and frequency of UL and DL slots, directly influences the timing and delay characteristics experienced by user equipment in both directions.

#### A. 5G TDD Framing and Slot Structure

5G New Radio supports flexible Time Division Duplexing (TDD), allowing dynamic assignment of uplink and downlink symbols within a radio frame, as specified in 3GPP TS 38.213 [12]. Radio frames are composed of slots, each containing 14 OFDM symbols under normal cyclic prefix. These slots are assigned as downlink (DL), uplink (UL), or flexible (S), depending on the configured pattern. Each slot corresponds to 0.5 ms for numerology $\mu = 1$ (30 kHz subcarrier spacing). The distribution and spacing of UL and DL symbols directly influence the latency experienced by uplink and downlink transmissions.

#### B. Uplink Access and Scheduling Behavior

In standalone 5G deployments, uplink data transmission begins with a Scheduling Request (SR) sent by the User Equipment (UE). The gNB (5G Base Station) processes this request and, upon acceptance, allocates uplink resources via

a downlink control grant. The UE then transmits the data over the Physical Uplink Shared Channel (PUSCH) in the next available UL slot. When UL symbols are sparse or misaligned with scheduler timing, this process can introduce significant waiting time. Note that the situation in the downlink direction is less problematic as the gNB has full control over transmissions and decides alone about the next DL slot to send data to the UE. To add to that, frames have often more DL slots than UL slots.

#### C. Delay Decomposition and Measurement Approach

To analyze uplink and downlink latency with fine granularity, we leverage detailed logging from the gNB, which records key protocol events with slot-level precision. Since each slot in our configuration spans 0.5 ms, the timing of these events can be mapped directly to absolute time intervals.

* **Uplink Access and Transmission Delay**: The interval from when the UE becomes ready to transmit (which may involve sending a Scheduling Request, depending on TDD configuration and grant mode) to the reception of the last segment (MAC PDU) of the ping probe sent by the UE at the gNB. This component encompasses both any required scheduling or grant waiting time, and the time needed to deliver all packet segments (in case of segmentation) across one or more uplink slots.

* **Processing and Forwarding Delay at the gNB and Core**: The time from the reception of the last uplink MAC PDU for a given probe at the gNB until the corresponding reply (e.g., ICMP Echo Reply) is ready for downlink transmission at the gNB. This includes protocol stack processing at the gNB, any core network or application processing, and preparation of the response.

* **Downlink Access Delay**: The period from when the reply becomes available at the gNB until it is scheduled and transmitted to the UE.

* **UE Acknowledgment Delay**: The time from sending packet to the UE until the UE sends a SR to transmit an acknowledgement for the received probe.

Each component can be quantified in terms of slot numbers (0.5 ms per slot), based on timestamps and slot indices recorded. This decomposition allows the total observed RTT to be mapped directly to distinct steps in the scheduling, transmission, and processing pipeline of the 5G system. By associating each of these events with their corresponding slot numbers in the gNB log, we can express each delay component as a multiple of the slot duration (0.5 ms). This decomposition enables us to attribute total observed RTT to specific protocol and scheduling processes within the 5G system.

#### D. Measurement Methodology

To capture precise event timings, we modified the OpenAir-Interface source code to add lightweight logging at key protocol steps, ensuring no significant impact on real-time behavior. The resulting logs were analyzed with custom Python scripts to extract per-packet delay components. All analysis scripts and gNB configuration files are available in our public GitLab
repository<fnref n="1" />. We repeatedly probe the 5G Gateway from the UE. Each RTT measurement was repeated for every TDD configuration to ensure consistency. All experiments were performed under controlled, static radio conditions to isolate the effects of scheduling and TDD pattern on probe delay.

## IV. EXPERIMENTAL SETUP

We deployed a standalone 5G network on a single machine, running the OAI software stack for both the 5G core (5GC) and RAN. Core network functions were containerized with Docker for configuration consistency. While simulations such as RFSIM provide useful baselines, they do not operate under strict real-time constraints and so miss interactions that emerge at slot granularity. Our measurement-based approach using USRP N300 as radio unit (RU) and a Quectel RM500Q-GL 5G modem as user equipment (UE) exposes these effects directly on a real 5G SA system. Both RU and UE were placed in the R2Lab anechoic chamber [10] to ensure repeatable, interference-free conditions. Experiments were conducted on the Post-5G Blueprint, a SLICES-RI [11] service enabling reproducible 5G experimentation.

We tested four TDD configurations (2, 2.5, 5, and 10 ms). Probe packets were sent from the UE to the User Plane Function (UPF) in the uplink direction. We used `hping3` for flexible ICMP packet generation <fnref n="2" />, starting with one packet per second to establish a baseline, then increasing the frequency of probes to 700 µs between two probe packets to study high-load effects and scheduling interactions.

For each TDD and traffic condition, we collected UE-side packet traces using `tcpdump` and extracted RTTs for analysis.

## V. RESULTS

We evaluated the RTT for different TDD periodicities and analyzed the impact of scheduling configuration on delay. RTT distributions are first compared for 2, 2.5, 5, and 10 ms TDD frames, followed by an analysis of SR timing, and finally, we carry out a slot-level decomposition of RTT components for a representative case.

### A. RTT Distribution Across TDD Periodicities

Figure 1 shows RTT measurements for four TDD configurations using default OAI settings. While longer TDD periods generally lead to higher delay, our results reveal that the 2 ms configuration has greater RTT than both 2.5 ms and 5 ms, with the latter showing close performance. The 10 ms TDD frame, as expected, yields the highest RTT.

### B. Impact of SR Periodicity

To understand this behavior, we examined the SR (Scheduling Request) configuration according to 3GPP standards. Clause 9.2.4 of 3GPP TS 38.213 [12] specifies that the timing and frequency of SR transmission occasions are governed by a configured periodicity and offset. In OAI, the SR periodicity is fixed to 10 slots for all TDD configurations, which in the 2 ms

<figure><img src="image_1.png" /><figcaption>Fig. 1: RTT boxplot for TDD configurations with periods 2 ms, 2.5 ms, 5 ms, and 10 ms.</figcaption></figure>

frame misaligns with the slot structure and causes ineffective SR opportunities. As a result, certain nominal SR opportunities may fall within downlink-only slots, making it impossible for the UE to transmit the SR at the expected time. The UE must then wait until the next valid SR opportunity, which can significantly increase the access delay and consequently the RTT. This effect explains why the RTT for TDD 2 ms is higher than for TDD 2.5 ms and 5 ms, even though those configurations use longer TDD periodicities.

To verify this explanation, we modified the SR configuration for TDD 2 ms and 2.5 ms to 4 and 5 slots respectively, providing more frequent and better-aligned SR opportunities. As illustrated in Figure 2, this change resulted in a reduction in RTT, confirming the critical role of SR periodicity and its alignment with the TDD frame structure in achieving low-latency performance.

<figure><img src="image_2.png" /><figcaption>Fig. 2: Complementary CDF of RTT before and after optimizing SR configuration for TDD 2 ms and 2.5 ms.</figcaption></figure>

These findings demonstrate that, beyond the nominal TDD frame period, the careful configuration of scheduling parameters, particularly ensuring that SR periodicity aligns as a multiple of the TDD frame length in accordance with 3GPP specifications, can decisively impact uplink access delay and overall latency.

### C. Effect of High-Frequency Traffic

To further investigate uplink access delay, we repeated the experiments with a much shorter packet generation interval

<footnote n="1">¹https://gitlab.inria.fr/mtavasso/5g-oai-latency-analysis</footnote>

<footnote n="2">²https://github.com/antirez/hping</footnote>
(700 µs), such that the uplink buffer is frequently non-empty. In this scenario, as the UE sends Buffer Status Reports (BSR), the gNB allocates uplink grants directly, bypassing the need for an explicit SR request in most cases. As shown in Figure 3, the median RTT and its percentiles are reduced compared to the earlier case where ICMP packets were generated at the low rate of one probe per second.

<figure><img src="image_3.png" /><figcaption>Fig. 3: RTT boxplot for different TDD configurations with high-frequency (700 µs) packet generation.</figcaption></figure>

## D. Delay Decomposition for the 5 ms TDD Configuration

Figure 4 shows the RTT decomposition for the 5 ms TDD scenario. The blue area, covering the major portion of each RTT, represents *Uplink Access & Transmission*, which includes waiting for an uplink grant and transmitting the full set of MAC PDUs. The red area corresponds to the *RAN/Core Processing* delay, i.e., the time for packets to traverse the gNB protocol stack and UPF before being prepared for downlink transmission which is comparatively tiny. The green area represents the *Downlink Scheduling* delay, reflecting the waiting time until the packet is sent in a downlink slot. Finally, the purple area shows the *UE Acknowledgment Delay*, i.e., the interval from receiving the downlink packet until the UE sends the SR for RLC ACK. The overlaid solid line represents the RTT measured at the MAC layer using `tcpdump`. The gap between the stacked delay components and the measured RTT is explained by packets arriving just after an SR opportunity, forcing them to wait until the next available one—adding up to a full TDD frame (5 ms) in the worst case.

## VI. CONCLUSION AND FUTURE WORK

In this paper, we investigated the factors influencing packet delay in standalone 5G deployments, emphasizing the role of TDD configuration and SR periodicity. Our results showed that while longer TDD periodicities typically increase packet delay, misaligned SR periodicities could lead to higher delays even at shorter TDD intervals. Through the delay decomposition, we further highlighted uplink access and scheduling as the predominant latency sources.

While the study provides detailed insights into RAN-related delay mechanisms, its scope is limited by the use of a single UE type and a colocated 5G core, which may not capture all factors of real-world deployments. Moreover, the

<figure><img src="image_4.png" /><figcaption>Fig. 4: Per-packet RTT decomposition for the 5 ms TDD configuration.</figcaption></figure>

findings are demonstrated on OAI and values may vary with other stacks or commercial deployments. Future work will extend this analysis to develop end-to-end delay models that incorporate both radio and core network segments, validated against experimental data. In addition, a complementary study of throughput alongside latency will be conducted to provide a more comprehensive characterization of 5G performance.

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