⚡ electrical engineering

Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

This paper addresses the inconsistency in 3GPP ISAC channel simulations by implementing the standardized model, conducting a comprehensive calibration analysis against reference results, and releasing the open-source simulator along with datasets to ensure reproducibility.

Original authors: Chien-Han Wu, Ming-Chun Lee, Ta-Sung Lee

Published 2026-06-08

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Chien-Han Wu, Ming-Chun Lee, Ta-Sung Lee

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to build a perfect, virtual "digital twin" of a city to test how a new 6G network will work. This network has a superpower: it doesn't just send text messages; it can also "see" its surroundings, like a radar, to detect cars, people, and buildings. This is called Integrated Sensing and Communication (ISAC).

To make sure everyone's digital city works the same way, the global standards group (3GPP) wrote a massive, complex rulebook (TR 38.901) on how to build these virtual channels. However, the rulebook was like a recipe written in a foreign language with some missing steps. If two different teams tried to bake the cake using the same recipe, they might end up with two very different-tasting cakes, even though they both claimed to follow the rules.

The Problem: The "Black Box" Recipe
The authors of this paper (a team from a university in Taiwan) realized that because the rulebook was so complicated and vague in places, different engineers were building simulators that didn't agree with each other. One team's "virtual car" might move differently than another team's, causing confusion and errors in testing.

The Solution: The Master Chef's Guide
The team decided to build their own simulator based on the 3GPP rulebook and then act as "master chefs" to figure out exactly how to make it taste exactly like the reference cake the standards group provided.

Here is what they did, broken down into simple concepts:

1. The Two Types of "Echoes"

In this virtual world, signals bounce off things in two ways:

The Target Channel (The "Flashlight" Effect): Imagine shining a flashlight at a specific object, like a car. The light hits the car and bounces back to you. The simulator has to calculate the distance, the angle, and how shiny the car is (its "Radar Cross Section"). This is the "Target Channel."
The Background Channel (The "Room" Effect): Imagine standing in a room and shouting. Even if no one is there, the sound bounces off the walls, floor, and ceiling. This is the "Background Channel." In a "monostatic" setup (where the sender and receiver are in the same device), the simulator has to invent "virtual listeners" in the room to figure out how the sound bounces around.

2. The Calibration: Matching the Fingerprint

The authors didn't just build the simulator; they spent a lot of time calibrating it. Think of calibration like tuning a musical instrument. You play a note, compare it to a perfect reference tone, and adjust the strings until they match perfectly.

They compared their simulator's output against the "official" results provided by big companies in the 3GPP group. They looked at specific "fingerprints" of the signal:

Coupling Loss: How much signal strength is lost? (Like how quiet your voice gets when you shout across a canyon).
Delay Spread: How long does it take for the echoes to arrive? (Like hearing an echo in a cave).
Angular Spread: How wide is the "fan" of the signal? (Like how wide a flashlight beam spreads).

3. The "Gotchas" (Hidden Details)

The most valuable part of the paper is the list of "traps" they found. These are tiny details in the rulebook that, if interpreted differently, ruin the calibration. They found five major "gotchas":

Height Limits: The rules for how signals fade change if a drone flies too high. The simulator had to switch rules at a specific height.
Where to Put the Targets: Should the virtual cars be spread out evenly across the whole map, or just in the middle of the city blocks? The paper found that the "official" results actually matched a specific distribution better, even if the rulebook sounded like it suggested another.
The Mirror Effect: In a monostatic setup (sender/receiver in one box), the path out and the path back are physically the same. But the math for angles and phases needs to be flipped like a mirror image. If you don't flip them correctly, the math breaks.
Filtering the Rays: The simulator generates thousands of invisible "rays" of light. Some rules say to throw away rays that come from very low angles (like rays skimming the ground). The authors found that sometimes keeping these rays actually matched the reference data better, suggesting the rulebook might be slightly ambiguous.
Picking the Winners: When checking the results, you can't check every single car in the simulation. You have to pick the top few. The authors figured out exactly how many to pick and which ones (the ones with the strongest signal) to make the numbers match the official report.

4. The Result: A Shared Blueprint

After fixing all these details, their simulator finally produced results that matched the 3GPP reference data almost perfectly.

The Big Takeaway:
The authors didn't just build a tool; they built a shared language. They realized that without a clear guide on how to interpret the rules, everyone was speaking a different dialect. By documenting these hidden details and releasing their code as open-source (free for anyone to use on GitHub), they are giving everyone a "Master Chef's Guide" to ensure that when different companies build 6G ISAC systems, they are all baking the exact same cake.

In short: They took a confusing, complex rulebook, figured out the hidden instructions, built a perfect test version, and gave the instructions to the world so everyone can build 6G sensing systems that actually work together.

Technical Summary: Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

Problem Statement
Integrated Sensing and Communication (ISAC) is a cornerstone technology for 6G systems, requiring accurate channel modeling for performance evaluation. While the 3GPP RAN1 working group has recently standardized an ISAC channel model and calibration procedure (documented in TR 38.901), implementing this model presents significant challenges. The methodology is highly complex, involving numerous parameters and detailed procedures. Crucially, the 3GPP reports lack fully explicit implementation details, leading to ambiguities in interpretation. Consequently, different implementations of the same specification often yield inconsistent or unsynchronized simulation results. Furthermore, practical aspects of calibration and the interpretation of specific assumptions are often unclear to researchers not deeply involved in 3GPP activities, hindering the development of interoperable, compliant simulators.

Methodology
To address these inconsistencies, the authors implemented a 3GPP-compliant ISAC channel simulator based on TR 38.901 and conducted a comprehensive calibration analysis. The methodology involved:

Simulator Development: The authors built a simulator covering both the Target Channel (cascaded path from transmitter to target to receiver) and the Background Channel (direct and environmental multipath).
- For the Target Channel, the implementation focuses on coupling forward and backward propagation links for the same scattering point, generating path power based on Radar Cross Section (RCS) and link conditions, and calculating delays based on 3D distances and indirect path components.
- For the Background Channel, the authors addressed the monostatic case (where transmitter and receiver are co-located) by introducing three virtual Reference Points (RPs) to generate equivalent propagation paths, applying specific angular and filtering rules defined in the standard.
Calibration Procedure: The simulator was calibrated against reference results reported by various companies in 3GPP (specifically report R1-2506616). The study utilized standard simulation setups for both FR1 (6 GHz) and FR2 (30 GHz) across outdoor (UMi, UMa) and indoor (InH) scenarios.
Metric Evaluation: The authors evaluated the simulator using three primary metrics defined in 3GPP:
- Coupling Loss: Both large-scale (pathloss, large-scale RCS, shadow fading) and full-scale (including small-scale fading).
- Delay Spread (DS): Temporal dispersion of the channel.
- Angular Spread (AS): Angular dispersion, including Azimuth and Zenith spreads.
Investigation of Implementation Details: A critical part of the methodology involved identifying and testing specific implementation choices that are not explicitly detailed in the standard but significantly impact results. These included sensing-target selection, monostatic coefficient construction, ZOA-based filtering, indoor target distribution, and the selection of calibration pairs.

Key Contributions
The paper makes four primary contributions:

Implementation: Development of a fully 3GPP-compliant ISAC channel simulator with detailed discussions on generating both target and background channels.
Validation: A comprehensive calibration study demonstrating that the developed simulator reproduces the intended statistical behavior of the 3GPP ISAC channel model across representative scenarios and metrics, matching 3GPP reference results.
Identification of Critical Details: The authors identify and discuss several implementation-dependent factors that materially affect calibration outcomes. These include:
- Sensing-target selection: How targets are chosen for calibration pairs.
- Monostatic coefficient construction: Specific handling of angle and phase coefficients for co-located transceivers.
- ZOA-based filtering: The impact of discarding rays with specific Zenith Angles of Arrival.
- Indoor target distribution: The difference between uniform distribution over a convex hull versus the entire area.
- Calibration pair selection: The number of Transmitter-Receiver pairs ( $N$ ) selected based on coupling loss.
Open-Source Release: To facilitate reproducibility and further research, the simulator, relevant datasets, and calibration results have been released as an open-source project on GitHub.

Results
The calibration results indicate that the developed simulator successfully aligns with the reference results reported by 3GPP companies.

Target Channel: The Cumulative Distribution Functions (CDFs) for coupling loss, delay spread, and angular spread in scenarios like UMa-AV and UMi Human closely match the 3GPP references.
Background Channel: Similar agreement was observed for the UMa-AV scenario.
Discrepancies and Sensitivity: The study highlighted that while the overall statistical behavior matches, minor variations exist between different companies' reported results. More importantly, the authors found that specific implementation choices lead to noticeable deviations:
- Target Distribution: Using a uniform distribution over the entire area (rather than the convex hull of the BS deployment) provided a better match to 3GPP reference results for indoor scenarios, despite the standard suggesting the latter.
- ZOA Filtering: In the UMa-AV background channel, results without ZOA-based ray filtering matched the reference data better than those with filtering, suggesting a potential inconsistency or nuance in the standard's description for specific scenarios.
- Monostatic Coefficients: Specific handling of phase and angle coefficients for the STX-SPST and SPST-SRX links was found to be critical for accuracy.

Significance
The paper asserts that its primary significance lies in bridging the gap between the high-level 3GPP specifications and practical, reproducible implementation. By providing a verified simulator and explicitly documenting the "hidden" implementation details that affect calibration, the work enables the development of interoperable ISAC simulators. The authors emphasize that proper calibration is critical for ensuring consistency with 3GPP reference results and that documenting implementation assumptions is necessary to avoid the inconsistencies currently observed in the field. The open-source release serves as a baseline for future research and standardization efforts in ISAC channel modeling.

1. The Two Types of "Echoes"

2. The Calibration: Matching the Fingerprint

3. The "Gotchas" (Hidden Details)

4. The Result: A Shared Blueprint

Technical Summary: Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

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