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REVIEW 4 major objections 4 minor 1 cited by

Open-Source 5G Core Platforms: A Low-Cost Solution and Performance Evaluation

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that among open-source 5G cores, Open5GS gives the best control-plane latency, OpenAirInterface the highest data rates, and Free5GC the lowest resource consumption.

desk verdict Useful real-SDR benchmark of three open-source 5G cores, but missing confidence intervals and DPDK configuration transparency keep the rankings from being definitive. read the letter →

arxiv 2412.21162 v1 pith:MJMDBLXK submitted 2024-12-30 cs.NI

Maria Barbosa , Marcelo Silva , Ednelson Cavalcanti , Kelvin Dias This is my paper · ORCID
classification cs.NI
keywords 5Gcorenetworkopen-sourceOpen5GSFree5GCOpenAirInterfacesoftware-definedradioperformanceevaluationCOTShardware
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that among open-source 5G core platforms, no single implementation dominates a low-cost standalone deployment. Running three open-source 5G cores against the same software-defined-radio access network and a commodity server, it finds a split result: Open5GS has the fastest registration and session-establishment signalling, OpenAirInterface delivers the highest data rates and lowest user-plane latency, and Free5GC consumes the least CPU and RAM. The finding matters because private and research 5G networks on commercial off-the-shelf hardware are becoming feasible, and operators can choose the core software according to their workload rather than settling for one overall winner.

What carries the argument

The central instrument is the testbed itself. It uses a single OAI-based gNodeB implemented with an Ettus B210 SDR and a log-periodic antenna, connected over a Docker network to whichever core is under test, with the same commodity server hosting all cores. Keeping the RAN and host fixed makes the measured differences in $\Delta T_r$, $\Delta T_s$, throughput, latency, video loading time, CPU, and RAM attributable to the core platforms. The registration and PDU session establishment signalling flows define the control-plane metrics, while Iperf3 and a Flask-based video server define the data-plane metrics.

What would settle it

Replace the OAI-based gNodeB with an independent or commercial gNodeB and repeat the registration, session-establishment, throughput, latency, and resource tests; if OAI no longer has the highest data rates or Open5GS no longer has the fastest control plane, the reported ranking is an artifact of the chosen RAN.

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Extended reading notes

Core claim

The central claim is a comparative verdict reached through a real end-to-end 5G Standalone testbed built from a software-defined radio running an OAI-based gNodeB, a commercial smartphone, and each candidate core deployed as Docker containers on the same Intel Xeon server. For the control plane, Open5GS posted the lowest registration time ($\Delta T_r$) and session establishment time ($\Delta T_s$); Free5GC and OAI were roughly 13% and 40% slower on registration and 11% and 17% slower on session establishment. For the data plane, OAI reached about 100 Mbps download in a congested Iperf3 path, roughly 50% higher than Free5GC, with about 10% lower latency; Open5GS sat in between. For resources, Free5GC used about 97% less CPU than OAI in both planes, while Open5GS used substantially more RAM than the other two. The paper also qualitatively compares four platforms, adding SD-Core, but excludes SD-Core from performance tests because it requires Kubernetes instead of Docker.

Load-bearing premise

The measured differences are treated as properties of the three core platforms, which assumes that using the same OAI-based gNodeB and server gives each core a fair and equivalent radio access network; if OAI's RAN is better tuned to its own core or the other cores are not optimally configured, the ranking could change.

Editorial extensions

If this is right

  • A low-cost private 5G standalone network on commodity hardware is workable; the paper demonstrates it with SDRs and a single server.
  • Operators prioritizing fast attach and session setup should choose Open5GS, at least at the tested scale.
  • Operators prioritizing user throughput and low data latency should choose OAI, accepting higher CPU consumption.
  • Resource-constrained edge deployments could choose Free5GC, accepting lower data rates.
  • The choice of core software is workload-dependent, so performance comparisons should report control-plane, data-plane, and resource metrics together.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that the rankings may shift under multi-UE load, since all measurements use a single phone; a natural extension is scaling the UE count and watching whether Open5GS's control-plane lead survives.
  • Because the RAN is OAI's own gNodeB, the comparison may be slightly favorable to OAI; swapping in an independent RAN implementation would test whether OAI's data-plane lead is intrinsic or a product of tight integration.
  • The split results suggest a hybrid architecture—Open5GS control plane with an OAI or DPDK-accelerated user plane—could combine low signalling latency with high throughput, which no single platform in the study achieves.
  • Free5GC's dramatically lower CPU use may make it the default choice for resource-constrained edge nodes, but only at traffic levels where its lower throughput is acceptable.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This paper reviews four open-source 5G core platforms (Free5GC, OAI, Open5GS, SD-Core), compares their features qualitatively, and experimentally evaluates three of them (SD-Core excluded) in a 5G Standalone testbed built from an OAI gNodeB over an Ettus B210 SDR and a COTS server. The measured metrics are registration and PDU-session establishment delays, end-to-end throughput, latency, video-streaming performance, and CPU/RAM consumption. The central claim is that Open5GS has the best control-plane latencies, OAI achieves the highest data rates, and Free5GC has the lowest resource consumption, so no single core is best for all low-cost private deployments.

Significance. The paper addresses a useful and timely gap: most prior quantitative work uses simulated UEs or RAN, whereas here the comparison is carried out on a real SDR-based SA testbed with a commercial handset. The qualitative feature comparison in Table I is a convenient resource, and the experimental setup is plausible and clearly described at the hardware level. If the performance ranking were fully substantiated, the paper would be a valuable practical guide for selecting an open-source 5GC in low-cost deployments. However, the ranking is currently supported only by point estimates and by a testbed description that does not specify key software configuration choices, so the quantitative conclusions are preliminary rather than definitive.

major comments (4)
  1. [Section VI.A, Figures 5 and 6, Table IV] The paper states in Section VI that 30 samples were collected to establish a 95% confidence interval, but no confidence intervals, standard errors, or significance tests are reported for any control-plane metric; Figures 5 and 6 show only point estimates. Moreover, Table IV's latency values are all within one standard deviation of each other (OAI 12.7±3.02 ms, Open5GS 13.6±3.32 ms, Free5GC 14.14±3.77 ms), so the claimed 10% latency advantage may be noise. The abstract's control-plane latency claim rests on means without dispersion information, and the reported differences are 11-17%, which is a load-bearing statistical gap. Report the confidence intervals or provide error bars/boxplots, and if the intervals overlap, soften the claims accordingly.
  2. [Sections III and V] Section III states that OAI and Open5GS have optional DPDK/VPP data-plane configurations while Free5GC does not (Table I), but Section V's testbed description never says whether these optimizations were enabled for the UPFs, nor does it give the 5GC release or commit identifiers for the three platforms. If the OAI and Open5GS UPFs ran with DPDK/VPP and Free5GC did not, the observed throughput and CPU-consumption gaps (50% download throughput difference and 97% CPU difference) are explained by an asymmetric configuration rather than by the platforms' architectures. The paper must state the exact default or tuned configuration used for each core, including whether acceleration was enabled, and should add a sensitivity discussion.
  3. [Section V] All three cores are tested behind the same OAI-based gNodeB, which is part of the same software project as OAI's core. This creates a potential fairness confound: OAI's RAN may be tuned or informally better integrated with OAI's 5GC, while Free5GC and Open5GS have to interoperate through the same RAN stack. The paper does not discuss this possibility or provide RAN-side validation (e.g., signal quality, retransmission counts, or scheduler behavior) across the three runs. At least a discussion of this limitation is needed to defend attributing 'OAI highest data rates' to the core platform rather than to the RAN-core pairing.
  4. [Section VI.C] Resource consumption is a central part of the paper's claim, but Figure 9 reports only average CPU/RAM values; although Section VI says 50 samples were collected for resource consumption, no standard deviations, confidence intervals, or statistical tests appear. The 85%, 45%, and 97% CPU-reduction statements in Section VI.C are therefore not substantiated, particularly given the DPDK/VPP confound noted above. Add dispersion information and state whether the CP and UP measurements were taken under identical signaling and data-plane loads.
minor comments (4)
  1. [Section II-A] The text contains ungrammatical phrases such as 'the between NFs' and 'exchanged one the 5GC NFs'; these should read 'the communication between NFs' and 'exchanged among the 5GC NFs'.
  2. [Section II-A] The description of communication models C and D appears garbled relative to 3GPP TS 23.501; the sentence about the consumer communicating directly with the NRF or via the SCP conflates discovery with communication, so the model definitions should be clarified.
  3. [Table II and Section V] The related-work table marks 'This Paper' as covering SD-Core, but SD-Core is excluded from the quantitative evaluation; this scope should be stated explicitly in the table footnote or in Section V.
  4. [Figures 5 and 6] The captions and axes should indicate the sample size and the type of error bar or dispersion display used, especially once confidence intervals are added.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports direct empirical measurements of three open-source 5GC platforms and draws conclusions from observed data, not from fitted parameters or self-referential derivations.

full rationale

This paper is an empirical, comparative measurement study. It builds a real 5G standalone testbed using a common OAI-based gNodeB and then measures control-plane registration and session-establishment times, data-plane throughput and latency, and CPU/RAM consumption for Free5GC, OAI, and Open5GS. There is no derivation chain in which an output is defined in terms of an input, no fitted parameter is later renamed as a prediction, and no load-bearing uniqueness or ansatz is imported from the authors' prior work. The conclusions—Open5GS has the best control-plane latencies, OAI has the highest data rates, and Free5GC has the lowest resource consumption—are direct observations of the measured quantities. Potential threats to validity, such as unstated DPDK/VPP configuration choices or the absence of confidence intervals for latency differences, concern experimental fairness and statistical interpretation, not circularity. The paper is self-contained as a measurement study and does not reduce to its own assumptions by construction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities are present; the paper is an empirical benchmark. The central claims rest on the assumptions that the platforms implement 3GPP correctly, that the OAI-based RAN is a fair common baseline, and that the measurement environment is stable.

assumptions (3)
  • domain assumption 3GPP procedures are correctly implemented by the tested platforms
    The evaluation assumes that the measured registration and session establishment times reflect 3GPP-compliant procedures; compliance is not independently verified in this study (Section VI).
  • domain assumption OAI gNodeB provides an equivalent RAN for all three cores
    Section V uses OAI for the gNodeB for all cores; the paper does not test whether each core behaves identically with another RAN, and OAI's integration with its own RAN may bias results.
  • domain assumption Measurement environment is stable across tests
    No mention of RF interference control or test isolation; the testbed description (Table III) lists hardware but not environmental conditions, so results may be affected by uncontrolled variables.

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Cite this review

Pith. "Pith review of Open-Source 5G Core Platforms: A Low-Cost Solution and Performance Evaluation." pith.science (2026). https://pith.science/paper/MJMDBLXK

@misc{pith2026241221162,
  author       = {Pith},
  title        = {Pith review of: Open-Source 5G Core Platforms: A Low-Cost Solution and Performance Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MJMDBLXK}},
  note         = {Machine review of arXiv:2412.21162}
}
read the original abstract

An essential component for the Fifth Generation of Mobile Networks deployments is the 5G Core (5GC), which bridges the 5G Radio Access Network (RAN) to the rest of the Internet. Some open-source platforms for the 5GC have emerged and been deployed in Common Off-the-Shelf (COTS)-based setups. Despite these open-source 5GC initiatives following the 3GPP specifications, they differ in implementing some features and their stages in the timeline of 3GPP releases. Besides that, they may yield different performance to metrics related to the data and control planes. This article reviews the major open-source 5GC platforms and evaluates their performance in a 5G Standalone (SA) COTS-based testbed. The results indicate that Open5GS provides the best latencies for control plane procedures, OpenAirInterface offers the highest data rates, and Free5GC has the lowest resource consumption.

Figures

Figures reproduced from arXiv: 2412.21162 by the authors.

Figure 2
Figure 2. Registration Signaling Flow. The flows show two signaling: one between the UE and AMF, called Non-Access Stratum (NAS) signaling, and the between NFs that use SBI to communicate via the HTTP pro￾tocol. NF-to-NF communication can be a direct model where the NF consumer interacts directly with the NF producer, without NRF (Model A) or using a service discovery with NRF (Model B). On the other hand, in an indirect mode… view at source ↗
Figure 3
Figure 3. PDU Session Establishment Signaling Flow. [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Architecture of the 5G SA network. Table III presents the specifications for setting up the 5G network, such as those of the Docker Host server and the mobile device used. The testbed implements a Docker network [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Registration Time Per 5GC [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: PDU Session Establishment Time Per 5GC. small-sized packets, ensuring measurement is not affected by factors other than latency. The average values (x¯) and the Standard Deviation (s) for each platform appear in Table IV. According to the results shown in the table, OA…
Figure 7
Figure 7. Figure 7: End-to-End Throughput Evaluation [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: End-to-End Latency Evaluation. C. Resource Consumption In addition to evaluating the control and data plane metrics, it is necessary to assess the hardware resources consumed for the signaling presented in Section II-A (representing CP consumption) and the data traffic…
Figure 9
Figure 9. Figure 9: Average Resource Consumption. The figure shows that OAI exhibited the highest CPU con￾sumption for both the CP and UP. On the other hand, Open5GS demonstrated 85% and 45% lower consumption for the CP and UP, respectively, when compared to OAI. Free5GC had the lowest CP…

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Forward citations

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Reference graph

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