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REVIEW 3 major objections 7 minor 26 references

Enhancing Open RAN Digital Twin Through Power Consumption Measurement

T0 review · 3 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Measured O-RAN power consumption stays nearly flat as network load rises, so energy savings should come from switching components off rather than scaling active ones.

desk verdict Useful RU power measurements for O-RAN splits, but DU/CU claims rest on host-level RAPL estimates. read the letter →

arxiv 2507.00928 v1 pith:EGBCNQ7P submitted 2025-07-01 eess.SP

Ahmed Al-Tahmeesschi , Yi Chu , Josh Shackleton , Swarna Chetty , Mostafa Rahmani , David Grace , Hamed Ahmadi This is my paper · ORCID
classification eess.SP
keywords O-RANOpenRANpowerconsumptionfunctionalsplitPRButilisationenergyefficiencydigitaltwinsoftware-definedradio
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 reports measured power consumption for the components of two Open RAN (O-RAN) deployments—Split 8 and Split 7.2b—under varying Physical Resource Block (PRB) utilisation, the share of scheduled radio resource blocks, from 0 to 100 percent. The authors aim to establish how much energy actually scales with network load and to provide the empirical data needed to build a digital twin of an O-RAN deployment. Their central finding is that power consumption in Radio Units (RUs), Distributed Units (DUs), and Central Units (CUs) stays nearly flat as load increases: the RU barely moves and the DU/CU draw only a few extra watts from idle to full load. This matters because it suggests that load-adaptive power scaling inside active components has limited headroom, and that energy-efficiency strategies should instead target switching off radio chains or entire components when traffic is low. The authors also fit quadratic models to the Split 8 DU/CU measurements for use in digital-twin simulations.

What carries the argument

The load-bearing apparatus is a pair of physical O-RAN testbeds instrumented so that power can be read per component while PRB utilisation is swept. Split 8 uses a USRP software-defined radio as the RU with srsRAN-based DU and CU; Split 7.2b uses commercial Benetel RAN550 and RAN650 RUs with a commercial DU/CU stack. RU power is read from a metered power distribution unit, while server DU/CU power is estimated with the Linux powerstat tool, which uses Intel RAPL counters and a power model. The two functional splits—Split 8 separating RF from PHY, and Split 7.2b separating low-PHY from high-PHY—define where baseband processing is done and therefore where load-dependent energy could appear. The measured power-versus-PRB-utilisation curves, plus the quadratic fits for Split 8 DU/CU, are the machinery that carries the argument.

What would settle it

Measure the same DU and CU servers with a calibrated wattmeter while sweeping PRB utilisation from 0 to 100 percent; if total server power rises by much more than the few watts reported here, the claim that O-RAN power is largely load-independent would fail.

Watch

Extended reading notes

Core claim

The paper's central discovery is that O-RAN power consumption is dominated by a constant base load rather than by traffic-dependent processing. For the Split 8 USRP-based RU, mean power stays between 43.1 W and 45.0 W across PRB utilisation from 0 to 100 percent; the commercial Benetel RAN550 indoor RU holds at roughly 28–30 W and the RAN650 outdoor RU at 44–46 W. The server-hosted DU and CU show more movement but still modest: in Split 8 the combined DU/CU rises from 119.5 W at zero load to 125.2 W (uplink) and 141.6 W (downlink) at full load, and in Split 7.2b the DU and CU each vary by only a few watts across the whole sweep. Downlink power is consistently higher than uplink, matching the higher downlink throughput. The paper concludes that because the load-dependent portion is small, O-RAN energy optimisation should concentrate on switching radio chains or components off rather than on fine-grained scaling of active components, and it provides quadratic fits of the Split 8 DU/CU measurements as a basis for digital-twin energy modelling.

Load-bearing premise

The load-bearing premise is that the powerstat tool's estimate of server power (based on processor energy counters, not a wattmeter) is accurate; if background server activity biases that estimate, the reported DU/CU power numbers and the fitted quadratics would misrepresent real hardware draw.

Editorial extensions

If this is right

  • Energy-saving algorithms for O-RAN should prioritise switching off radio chains or whole components during low traffic, because the load-dependent part of the power draw is small.
  • Digital-twin energy models should treat a large constant base-power term as the dominant contributor and add a small traffic-dependent correction, using the fitted curves as a starting point.
  • Downlink power exceeds uplink at full load on both testbeds, so downlink-heavy configurations have slightly more absolute power to recover, but the recoverable load-dependent fraction remains small.
  • Functional-split choice affects where the constant cost sits: the SDR-based Split 8 RU shows slightly more variation, while the commercial Split 7.2b RUs stay almost perfectly flat.
  • PRB utilisation alone appears sufficient as the load variable to capture the dominant energy behaviour on these testbeds, which simplifies measurement for digital-twin calibration.

Reading between the lines

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

  • If this flat-power pattern holds on other O-RAN hardware, the practical energy lever shifts from improving baseband processing efficiency to cell-level sleep and radio-chain shutdown, and network energy models should be driven mainly by which components are active, not by instantaneous PRB utilisation.
  • Because the server-side figures come from a RAPL-based power model rather than a wattmeter, a direct metering pass on the same servers would show whether true DU/CU load scaling is even smaller or meaningfully larger; this is the cleanest test of the conclusion.
  • A natural next experiment is to measure power while actually switching off radio chains or placing RUs in sleep mode at low PRB utilisation, which would quantify the savings this paper's conclusion implies.
  • The quadratic fits are tied to these specific servers and RUs; using them in a digital twin for different hardware would require re-measuring the constant and linear coefficients rather than assuming they transfer.
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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

3 major / 7 minor

Summary. The manuscript reports an experimental measurement study of power consumption in O-RAN functional splits, specifically Split 8 and Split 7.2b, under varying PRB utilization. RU power is measured with a metered PDU for a USRP-based RU and for commercial Benetel RAN550/RAN650 RUs; DU and CU power is estimated with powerstat, which the text describes as an RAPL-based power consumption model. The measurements show RU power nearly constant and DU/CU power increasing only slightly with PRB utilization. The authors fit quadratic polynomials to the combined Split 8 DU+CU data and conclude that power does not scale significantly with load, recommending that energy optimization focus on switching off radio chains or components rather than on load-adaptive scaling. They further claim that these measurements provide foundations for developing accurate digital twins of O-RAN deployments.

Significance. The RU measurements are a genuine empirical contribution: they use a metered PDU, cover both functional splits and both uplink and downlink, and show a clear load-invariant pattern for the RU. If the DU/CU estimates were trustworthy, the conclusion would have practical implications for energy-saving strategies in O-RAN. The paper's recommendation to prioritize component-level on/off switching over fine-grained load-adaptive power scaling is a concrete, testable hypothesis. At present, however, the significance is substantially diminished by the unvalidated RAPL-based estimates for DU/CU and by the internally inconsistent quadratic model in Section V; the digital-twin claims are not yet substantiated.

major comments (3)
  1. [Section III, Tables IV, VI, VII] The DU/CU power measurements are not component-level and are not wall-power. The text explicitly states that server power was obtained with powerstat, 'based on intel RAPL,' and that the tool 'estimate[s] the power consumed based on a power consumption model.' Figure 2 shows that in Split 8 a single server hosts the CU, DU, RIC, and Core in one Docker image, and in Split 7.2b the CU server also hosts RIC and Core. RAPL measures CPU package energy (and on some platforms DRAM), not per-container or per-VNF energy, and it cannot attribute energy to the DU or CU. Tables IV, VI, and VII therefore report host/software-stack power, not DU/CU power. The observed flatness may be dominated by always-on RIC/Core/container overhead and Linux idle behavior, rather than by DU/CU processing characteristics. The central claim that DU and CU power are load-invariant is not supported by these data; the authors must either isolate the DU and CU (for example, separate hosts, container-level energy accounting with calibration, or direct AC measurement), or explicitly reframe the conclusion as applying to the whole server-side software stack.
  2. [Section V, Eqs. (1) and (2)] The fitted quadratic models are internally inconsistent with Table IV. At l=0, Eqs. (1) and (2) predict 61.06 W and 62.28 W, respectively, whereas Table IV reports 119.51 W at 0% PRB (and 58.34 W at idle). The fits appear to model the power increase over the idle baseline, but the text does not state this; as written, the model would badly mispredict the 0% PRB operating point that the digital twin would need. In addition, the coefficient 22.12 on l^2 in Eq. (2) implies that l is normalized to [0,1], while the text defines l as the PRB utilisation percentage; if l were a percentage, the predicted value at 100% PRB would be implausibly large. The equations must be corrected to include the baseline explicitly and the normalization of l must be defined unambiguously.
  3. [Section V, Fig. 3] The quadratic model is fit to the same data it is compared against, with no validation on held-out measurements, no cross-validation, and no goodness-of-fit or prediction-error statistics. Because the paper's stated purpose is to 'provide foundations for developing accurate digital twins' (Section I), the authors should demonstrate that the model can predict measurements not used in fitting, or at minimum report residual errors and a train/test split. As it stands, Fig. 3 is a curve fit to the training data; the predictive value of the model for digital-twin forecasting is untested.
minor comments (7)
  1. [Section III-A] The list of testbed components says 'including the RU, DU and DU'; this should read 'RU, DU and CU.'
  2. [Fig. 2 caption] The abbreviations 'fronthall,' 'midhall,' and 'backhall' should be corrected to 'fronthaul,' 'midhaul,' and 'backhaul.'
  3. [Section IV-B] The text contains typographical errors: 'receptively' should be 'respectively' and 'utlisation' should be 'utilisation.'
  4. [Section V] The symbol l is described as 'PRB utilisation percentage' but the fitted coefficients are consistent with l being a fraction in [0,1]; please define the variable and its range explicitly in the text and in the figure axis label.
  5. [References] Reference [17] is cited as a prior study measuring RU and DU power in an Open RAN-based LTE system, but the listed title refers to deep convolutional neural network based reinforcement learning for mobile network power saving; please verify that the citation matches the claimed content.
  6. [Title, Section I, Section VI] The paper emphasizes digital twins in the title, abstract, and conclusions, but it contains no digital twin simulation, use case, or demonstration of how the fitted power model improves digital twin accuracy; please either add a concrete digital twin experiment or temper the claims.
  7. [Tables VI and VII] The captions describe the values as power consumption in Watts, but these values are powerstat estimates, not direct measurements; the captions should note the estimation method.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the load-invariance conclusion follows directly from the measured tables, and the Section V quadratic fits are explicitly descriptive curve fits rather than out-of-sample predictions.

full rationale

The paper's central claim—that O-RAN power consumption does not scale significantly with PRB utilisation—is derived from the measurement tables in Section IV, not from a fitted model or from a self-citation chain. Tables III–VII report measured power at different PRB levels, and the qualitative conclusion in Section VI restates those observations ('the power consumption in O-RAN components... does not scale proportionally with PRB utilisation'). The only modelling step is in Section V, where the authors write 'the quadratic polynomial fits are given as...' and Fig. 3 shows the measured data alongside the fit. This is explicitly a fit to the same data it describes; it is not used to predict a held-out quantity or to derive the load-invariance conclusion. Under the stated rules, descriptive curve fitting of this kind is not circularity, even though external validation for digital-twin forecasting is absent. The self-citations ([5], [9], [11], [23]) appear only as background or related-work examples and are not load-bearing for any derivation. The powerstat/RAPL measurement caveat raised in Section III ('utilising powerstat which is an open-source energy monitoring functionality for machines (based on intel RAPL) and estimate the power consumed based on a power consumption model') is a measurement-validity and attribution concern about whether the reported DU/CU values represent component-level draws, not a circular-reasoning defect; it affects correctness risk rather than circularity. No equation in the paper reduces by construction to its own inputs, and no uniqueness or ansatz is imported from prior author work. Therefore the honest finding is no significant circularity, score 0.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The central empirical claim is supported by direct meter readings for RUs, but DU/CU power estimates rely on an uncalibrated tool model. The fitted quadratics introduce six free parameters, and the stated intercepts are inconsistent with the measured 0% values, suggesting an unstated normalization. No new entities are introduced.

free parameters (6)
  • Uplink quadratic coefficient (a_UL) in eq. (1) = 1.86
    Fitted to the five measured PRB utilization points for Split 8 combined DU/CU uplink power.
  • Uplink linear coefficient (b_UL) in eq. (1) = 4.3
    Fitted to the same uplink data points; controls the linear load response.
  • Uplink intercept (c_UL) in eq. (1) = 61.06
    Fitted intercept; does not match the measured 0% PRB value of 119.51 W, indicating possible normalization to an idle or baseline value that is not stated in the text.
  • Downlink quadratic coefficient (a_DL) in eq. (2) = 22.12
    Fitted to the downlink combined DU/CU power data; strongly convex.
  • Downlink linear coefficient (b_DL) in eq. (2) = -2.4
    Fitted to downlink data; negative linear term offsets the quadratic term at low loads.
  • Downlink intercept (c_DL) in eq. (2) = 62.28
    Fitted intercept; also inconsistent with measured 0% PRB value of 119.51 W, reinforcing the suspicion of unstated normalization.
assumptions (3)
  • domain assumption powerstat's RAPL-based model accurately estimates server power consumption
    Used for all DU and CU power values; the paper does not report calibration against a reference wattmeter, and the tool's model may not capture the specific server components.
  • domain assumption PRB utilization levels (0, 25, 50, 75, 100%) are controlled and representative of network load
    The paper does not describe the traffic generation method or how exactly PRB utilization is set and verified; 0% is defined as zero UEs attached, which is different from a connected UE using zero PRBs.
  • domain assumption The testbed configurations (n78/n77, 40 MHz, TDD patterns, 2x2 MIMO, single UE within 3 m) are representative enough to generalize the conclusions
    Measurements are from a single lab environment with one UE and specific hardware; the paper does not assess variability across deployments.

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Cite this review

Pith. "Pith review of Enhancing Open RAN Digital Twin Through Power Consumption Measurement." pith.science (2026). https://pith.science/paper/EGBCNQ7P

@misc{pith2026250700928,
  author       = {Pith},
  title        = {Pith review of: Enhancing Open RAN Digital Twin Through Power Consumption Measurement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EGBCNQ7P}},
  note         = {Machine review of arXiv:2507.00928}
}
read the original abstract

The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly within the Radio Access Network (RAN). This growing energy demand raises operational and sustainability challenges for mobile network operators, requiring novel solutions to enhance energy efficiency while maintaining Quality of Service (QoS). 5G networks are evolving towards disaggregated, programmable, and intelligent architectures, with Open Radio Access Network (O-RAN) spearheaded by the O-RAN Alliance, enabling greater flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach introduces new complexities, especially in terms of power consumption across different network components, including Open Radio Units (RUs), Open Distributed Units (DUs) and Open Central Units (CUs). Understanding the power efficiency of different O-RAN functional splits is crucial for optimising energy consumption and network sustainability. In this paper, we present a comprehensive measurement study of power consumption in RUs, DUs and CUs under varying network loads, specifically analysing the impact of Physical resource block (PRB) utilisation in Split 8 and Split 7.2b. The measurements were conducted on both software-defined radio (SDR)-based RUs and commercial indoor and outdoor RU, as well as their corresponding DU and CU. By evaluating real-world hardware deployments under different operational conditions, this study provides empirical insights into the power efficiency of various O-RAN configurations. The results highlight that power consumption does not scale significantly with network load, suggesting that a large portion of energy consumption remains constant regardless of traffic demand.

Figures

Figures reproduced from arXiv: 2507.00928 by the authors.

Figure 1
Figure 1. Illustration of functional splits. (a) Split 8, (b) Split 7.2b. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The physical deployments for different functional splits: (a) Split 8 and (b) Split 7.2b. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Comparison between measured and quadratic fit power [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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