Ns3 meets Sionna: Using Realistic Channels in Network Simulation

Anatolij Zubow; Falko Dressler; Sascha R\"osler; Yannik Pilz

REVIEW 3 major objections 6 minor 2 cited by

Ns3 meets Sionna: Using Realistic Channels in Network Simulation

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Ns3Sionna replaces ns-3's statistical radio models with ray-traced channels that are spatially and temporally correlated.

desk verdict A solid, useful ns-3/Sionna integration with a real caching design, but the 'more realistic' claim outruns the validation, which only checks free-space Friis agreement. read the letter →

arxiv 2412.20524 v1 pith:7NRATPMP submitted 2024-12-29 cs.NI

Anatolij Zubow , Yannik Pilz , Sascha Rösler , Falko Dressler This is my paper · ORCID

classification cs.NI

keywords raytracingns-3SionnaRTchannelmodelspatialcorrelationtemporalnetworksimulationcoherencetime

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

Ns3Sionna is a software module that lets the ns-3 network simulator compute wireless channels by ray tracing instead of by the simplified statistical and stochastic models it normally uses. The paper claims that for a given 3D scene, the module produces path loss and propagation delay estimates that are more realistic than existing ns-3 models, especially in how the channel evolves smoothly across space and time. Because multipath, shadowing, and frequency-selective fading are computed from the geometry and materials of the environment, the channel state information also becomes available for sensing applications. The authors back this with a free-space validation against the Friis formula and with indoor and outdoor example scenarios, and they show that pre-caching based on coherence time and point-to-multipoint ray tracing make the approach computationally feasible for small to medium networks.

What carries the argument

The load-bearing mechanism is Sionna RT, a GPU-accelerated ray tracer that computes radio propagation paths through a user-provided 3D scene with material properties, returning a channel impulse response for each transmitter-receiver pair. Ns3Sionna wraps this into two ns-3 models, SionnaPropagationLossModel and SionnaPropagationDelayModel, and adds a cache keyed by coherence time and channel reciprocity so that channels are not recomputed unnecessarily. To exploit parallelism, the framework asks Sionna to compute the full point-to-multipoint channel from a sender to all potential receivers, including virtual nodes at predicted future positions, in one call, which makes the expensive ray tracing affordable for simulations with a small to medium number of mobile nodes.

What would settle it

Run a measurement campaign in a real two-room indoor layout with an open door, recording received power and delay along a 1 m/s walking path; the central claim fails if Ns3Sionna does not reproduce the measured spatial correlation of shadowing and the sharp wall-induced attenuation more closely than ns-3's independent log-normal shadowing model.

Watch

Extended reading notes

Core claim

The central claim is that coupling Sionna RT's physically based ray tracing with ns-3 yields channel realizations, expressed as path loss, delay, and channel frequency response, that reflect the actual propagation environment rather than a probability distribution. In particular, the paper argues that Ns3Sionna's channel estimates are more realistic than ns-3's built-in models in terms of spatial and temporal correlation: a moving station sees smooth, correlated changes in received power as it moves through the 3D scene, and the channel remains stable over the coherence time. The module also provides a ray-tracing-based mobility model that keeps nodes inside the 3D geometry, and it exploits channel reciprocity, coherence time, and parallel point-to-multipoint computation to keep runtime practical.

Load-bearing premise

The entire approach assumes that Sionna RT, given a user-created 3D scene and material parameters, predicts channels accurately enough to represent real wireless propagation; the paper validates this only in free space against the Friis formula and offers no measurement campaign for its indoor and outdoor realism claims.

Editorial extensions

If this is right

Wi-Fi protocol simulations in indoor environments can now exhibit multipath fading, wall shadowing, and frequency-selective channels that standard ns-3 statistical models cannot produce.
Because channels are spatially consistent, positioning, sensing, and fine-grained channel state information studies can be done inside ns-3 without a separate propagation tool.
Coherence-time caching and point-to-multipoint computation keep the ray-tracing overhead practical for small to medium networks, enabling parameter sweeps that would otherwise take years.
The ray-tracing mobility model keeps nodes inside the 3D geometry and makes them bounce off obstacles, removing the unrealistic bounding-box random walk limitation.
The same design extends to LTE via the SpectrumChannel, to MIMO, and to reconfigurable intelligent surfaces, since Sionna RT already provides the needed primitives.

Reading between the lines

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

Not stated in the paper: the realism claim inherits all of Sionna RT's error; the free-space validation only checks agreement with Friis, so the indoor and outdoor advantage over ns-3 remains an assumption until a measurement campaign compares the two.
Not stated in the paper: Ns3Sionna could serve as a data generator for machine-learning indoor positioning, since its spatially correlated shadowing is a more realistic training distribution than independent log-normal draws.
Not stated in the paper: the pre-caching pattern, computing a point-to-multipoint channel and predicting future positions, is a general recipe for coupling expensive physical models into any packet-level simulator.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Dear [Name],

The short version: this is a serious engineering contribution—the first real integration of Sionna RT into ns-3, with a sensible caching and pre-computation scheme that makes ray-traced channels usable at packet level—but the paper overstates what it has demonstrated. The headline claim about "more realistic" indoor and outdoor channels is not backed by the validation section. Section V only compares free-space path loss against ns-3's Friis model. That check exercises the integration plumbing, not the physics. Any distance-dependent model would pass it.

What is genuinely new and good: the point-to-multipoint pre-caching, where one Sionna call computes channels from a sender to all potential receivers, is a smart use of the engine's parallelism. The virtual-node idea—simulating future node positions so their channels are computed speculatively—is also original relative to the ns-2 ray-tracing work they cite. The reciprocity cache keyed by coherence time is the right way to avoid redundant computation, and the paper honestly reports the costs: ray tracing is one to four orders of magnitude slower than ns-3's statistical models, and the authors say so plainly. The code is open source, the architecture is clearly described, and the related work is fair, with appropriate citations to Sionna and to Dricot and De Doncker without any self-citation inflation.

Soft spots, in proportion: the missing measurement validation is the main one. The indoor and outdoor example scenarios (Section VI) show plausible-looking spatial and temporal correlation, but there is no comparison to measured channels or even to ns-3's statistical models, so the stated superiority of the ray-traced channels over those models is an assertion, not a result. This is a missing-validation issue rather than an identified error—Sionna RT may well be accurate—but the manuscript does not supply the evidence. A secondary, minor issue: the benchmark numbers in Table I and Figures 9–11 are reported as single values with no error bars, which is fine when the gaps are orders of magnitude, but the GPU speedup factors (14.2548×, 7.9023×) imply a precision that likely isn't there. I'd also ask for a commit hash or dependency pins in the paper, though the repo may already provide that.

Who this is for: anyone doing system-level Wi-Fi or 6G protocol simulation who needs spatially and temporally consistent channels and is willing to pay a big runtime cost. It is a tools paper, and the tool looks real and usable.

Recommendation: send to peer review. The engineering is sound and the caching ideas are worth publishing, but the realism claim needs either a measurement-based comparison or softer language. A serious referee should ask for one of those.

Referee Report

3 major / 6 minor

Summary. The paper presents Ns3Sionna, a software framework that integrates the Sionna RT ray-tracing engine into the ns-3 network simulator. Two new ns-3 models (SionnaPropagationLossModel and SionnaPropagationDelayModel) offload channel computation to Sionna RT, and the framework adds a ray-tracing-based mobility model, caching of channel state information, channel reciprocity reuse, and parallel computation of point-to-multipoint channels. The authors claim that Ns3Sionna provides more realistic path and delay loss estimates than existing ns-3 propagation models, especially in spatial and temporal correlation, and they report example indoor and outdoor scenarios plus benchmark results showing large runtime overheads that are mitigated by caching and GPU-parallel predictive computation.

Significance. If the realism claim were properly supported, this would be a valuable open-source contribution: it would give the ns-3 community a way to run protocol-level simulations over ray-traced, scene-specific channels with spatial and temporal consistency, and the repository makes the implementation available. The paper also demonstrates useful engineering ideas, such as exploiting channel coherence time for caching and computing point-to-multipoint channels in one Sionna call to amortize ray-tracing cost. However, the central claim of the paper—that Ns3Sionna is more realistic than existing ns-3 models in indoor and outdoor environments—is not currently validated. The only quantitative validation is a free-space comparison against the Friis model, which is a self-consistency check of the integration rather than evidence for multipath, shadowing, or correlation accuracy. The indoor and outdoor examples are qualitative demonstrations without comparison to measured channels or to the ns-3 statistical models that the paper claims are less realistic. The performance study is useful and honestly reports large overheads, but it does not compensate for the missing validation of physical realism.

major comments (3)

[Section V, Figure 4] The validation section compares Ns3Sionna only with ns-3's Friis propagation loss model and constant-speed delay model in free space. This checks that the integration plumbing works, but it does not validate the paper's central claim of more realistic path and delay loss estimates in indoor and outdoor environments. A distance-dependent Friis-style model would pass this same test in free space, so the test does not exercise multipath, reflections, diffraction, wall penetration, material parameters, shadowing, or any correlation structure. The authors should add validation against measured channel data or against established ray-tracing benchmarks in representative indoor and outdoor scenes, including metrics such as path loss, delay spread, and spatial/temporal correlation.
[Section VI, Figures 6 and 8] The indoor and outdoor example scenarios are presented as demonstrations of the framework's output, but no quantitative comparison is made against either measured channels or the existing ns-3 propagation models mentioned in the introduction (e.g., LogDistance, ITU-R P.1238, or HybridBuildingsPropagationLossModel). As a result, the abstract's claim that Ns3Sionna 'provides more realistic path and delay loss estimates for both indoor and outdoor environments than existing ns-3 propagation models' is not supported by the evidence in the paper. The authors should run the same scenarios with the relevant ns-3 baseline models and report quantitative differences in path loss, delay, and correlation metrics.
[Section IV-B and Section V] The propagation delay is defined as the delay of the shortest path among all ray-traced paths. In the free-space validation this reduces exactly to the constant-speed delay d/c, so the delay comparison in Figure 4 is a tautology for the testing environment. The delay model's realism in multipath environments is not assessed at all; the paper reports no delay spread or first-arrival-path statistics for the indoor or outdoor scenarios. Please add delay-related metrics (e.g., RMS delay spread or excess delay) to the scenario evaluation, since delay estimation is part of the central claim.

minor comments (6)

[Section IV-A] The text says 'The first two points are addressed by caching' and then 'The second point is addressed as follows,' where the second sentence appears to refer to the third design point (avoiding far-away receivers). Please correct the numbering to avoid confusion.
[Section IV-C] There is a grammatical error in 'message passing as it gives are the possibility to run ns-3 and Sionna on different computers.' Please revise this sentence.
[Section VIII-A, Figure 9] The y-axis labels in Figure 9 appear to be missing an exponent (the text mentions differences of four orders of magnitude, but the axis shows '100' and '105'). Please format the axis with explicit powers of ten.
[Section VIII-B, Figure 11] Figure 11 has no y-axis label and the caption does not state what the speedup is relative to. Please clarify the baseline for the speedup computation.
[Section VIII-C] The term 'PHZ abstraction models' appears to be a typo for 'PHY abstraction models.' Please fix this and similar typos throughout the discussion.
[References [15] and [17]] The URLs in references [15] and [17] are missing the '://' separator (e.g., 'https:www.nsnam.org'). Please correct them.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: Ns3Sionna's channel outputs are computed from scene geometry and positions via Sionna RT; the free-space validation is a consistency check, not a fitted prediction.

full rationale

The paper's channel outputs—path loss, propagation delay, and CFR—are produced by Sionna RT from the 3D scene, material parameters, antenna settings, and node positions; they are not fitted to the abstract's target claims. The free-space check in Section V compares Ns3Sionna with ns-3's Friis and ConstantSpeed models and shows agreement (Fig. 4); this is a wrapper-consistency test and, in free space, ray tracing and Friis coincide for physical reasons, so the agreement is not an input to the indoor/outdoor realism claim. Spatial and temporal correlation are emergent from ray tracing over a scene and from caching over the coherence time (Sections IV-B and VI-A), not from parameters fitted to measured correlation. The abstract's 'more realistic' claim is not circular, but it is under-supported: the only quantitative validation is free-space Friis agreement, and no measurement campaign is given for the indoor/outdoor scenes, so the superiority claim is a correctness/validation gap rather than a self-referential derivation. The one self-citation ([2], supporting a peripheral statement that ns-3 is popular) is not load-bearing for any technical result, and the Sionna RT citations point to an external project, not to the authors' own prior work invoked to forbid alternatives. No equation in the paper is equivalent to an input by construction, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The framework adds no new physics. It assumes Sionna's ray tracing is accurate for the given 3D scene, uses the coherence-time formula as cache TTL, assumes reciprocity, and relies on user-provided scene and material data. No free parameters are fitted in the paper.

assumptions (5)

domain assumption Sionna RT accurately models electromagnetic wave propagation for the materials and scene geometry provided.
The paper's realism claim depends on Sionna's accuracy; the paper does not validate this with measurements, and Section V only checks free space against Friis.
domain assumption The coherence time formula (Eq. 2) is a valid expiry time for cached channel state information.
The cache TTL is computed from Eq. 2 in Section IV-B; this assumes the channel remains correlated for the cached interval and that reusing it does not distort temporal correlation.
domain assumption The user-provided 3D scene and material parameters faithfully represent the physical environment.
Any error in the scene or material properties directly propagates into path loss, delay, and CFR, so the realism claim is only as good as the scene.
domain assumption Channel reciprocity holds for cached links.
Section IV-B states the cache accounts for reciprocity, so A-to-B CSI is reused for B-to-A; this is valid for passive reciprocal channels but requires symmetric antenna behavior.
domain assumption Future node positions can be precomputed because mobility is independent of network traffic.
Used for virtual-node pre-caching in Section IV-A and Figure 2; if mobility depended on traffic, precomputed channels would be stale.

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Cite this review

Pith. "Pith review of Ns3 meets Sionna: Using Realistic Channels in Network Simulation." pith.science (2026). https://pith.science/paper/7NRATPMP

@misc{pith2026241220524,
  author       = {Pith},
  title        = {Pith review of: Ns3 meets Sionna: Using Realistic Channels in Network Simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7NRATPMP}},
  note         = {Machine review of arXiv:2412.20524}
}

read the original abstract

Network simulators are indispensable tools for the advancement of wireless network technologies, offering a cost-effective and controlled environment to simulate real-world network behavior. However, traditional simulators, such as the widely used ns-3, exhibit limitations in accurately modeling indoor and outdoor scenarios due to their reliance on simplified statistical and stochastic channel propagation models, which often fail to accurately capture physical phenomena like multipath signal propagation and shadowing by obstacles in the line-of-sight path. We present Ns3Sionna, which integrates a ray tracing-based channel model, implemented using the Sionna RT framework, within the ns-3 network simulator. It allows to simulate environment-specific and physically accurate channel realizations for a given 3D scene and wireless device positions. Additionally, a mobility model based on ray tracing was developed to accurately represent device movements within the simulated 3D space. Ns3Sionna provides more realistic path and delay loss estimates for both indoor and outdoor environments than existing ns-3 propagation models, particularly in terms of spatial and temporal correlation. Moreover, fine-grained channel state information is provided, which could be used for the development of sensing applications. Due to the significant computational demands of ray tracing, Ns3Sionna takes advantage of the parallel execution capabilities of modern GPUs and multi-core CPUs by incorporating intelligent pre-caching mechanisms that leverage the channel's coherence time to optimize runtime performance. This enables the efficient simulation of scenarios with a small to medium number of mobile nodes.

Figures

Figures reproduced from arXiv: 2412.20524 by the authors.

**Figure 2.** To speed up execution Ns3Sionna converts future node locations into locations of virtual nodes which can be calculated in parallel. an example of a single transmitter and two mobile receivers. Here we convert the future two receiver node locations into the location of two additional virtual nodes. Thereafter, the channel is computed towards all the four nodes enabling its efficient parallel computation. Note, a slig… view at source ↗

**Figure 4.** Validation of path loss and propagation delay in free-space [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (6 more)

**Figure 3.** Figure 3: Architecture of the Ns3Sionna be simulated, the center frequency, channel bandwidth, FFT size (i.e., number of OFDM subcarriers), used mobility model as well as general information about each node are passed from ns-3 to Sionna RT. During the simulation new channel pro…

**Figure 5.** Figure 5: Indoor scenario - two rooms and open door [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Results for indoor scenario VI. EXAMPLE SCENARIOS In this section we present results for two scenarios, indoor and outdoor, using Ns3Sionna. A. Indoor Scenario The indoor scenario consists of two adjacent rooms connected with an open door ( [PITH_FULL_IMAGE:figures/f…

**Figure 9.** Figure 9: shows the execution time on a host with AMD Ryzen 9 7950X (128 GByte RAM, 16 cores) to perform a single simulation run of 10 s. First, we can clearly see the increase 1 2 4 8 16 32 64 No. STAs 100 105 Execution time [s] Ns3Sionna (hT/hM) Ns3Sionna (lT/lM) Ns3Sionna (hT…

**Figure 10.** Figure 10: Caching performance of Ns3Sionna in execution time vs. pure ns-3. The increase is smallest in the stationary scenario (hT/zM) as the channel between each pair of nodes need only be computed once resulting in an increase of execution time for Ns3Sionna at around 3.6-73…

**Figure 11.** Figure 11: Speedup through predictive calculations factor of 14.2548× in a network with single STA. With larger network size the speedup decreases to a factor of 7.9023× as the maximum number of predictive calculations is limited by the available GPU memory size. With the RTX GP…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

VaN3Twin: the Multi-Technology V2X Digital Twin with Ray-Tracing in the Loop
cs.NI 2025-05 conditional novelty 7.0 of 10

VaN3Twin integrates ray-tracing into a full-stack V2X simulator to enable accurate multi-technology coexistence modeling and reports 50-70% better agreement with field measurements than prior tools.
Enabling Site-Specific Cellular Network Simulation Through Ray-Tracing-Driven ns-3
cs.NI 2025-08 conditional novelty 6.0 of 10

SioLENA builds full spatial channel matrices inside ns-3 from ray-tracing traces, giving 5G-LENA site-specific propagation while keeping its beamforming and scheduling code untouched.

Reference graph

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