⚡ electrical engineering

Framework for Indoor Wireless Propagation Modeling Through Wireless Insite

This paper presents a comprehensive framework for indoor wireless propagation modeling that integrates SketchUp for layout construction, Wireless Insite for ray tracing simulations, and MATLAB for channel parameter analysis to accurately predict coverage holes and optimize network performance.

Original authors: Arao Zau Macaia, Niraj Narayan, Rajeev Shukla, Aniruddha Chandra, Ondrej Zeleny, Radek Zavorka, Jiri Blumenstein, Ales Prokes, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski

Published 2026-03-24

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Arao Zau Macaia, Niraj Narayan, Rajeev Shukla, Aniruddha Chandra, Ondrej Zeleny, Radek Zavorka, Jiri Blumenstein, Ales Prokes, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski

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 set up a Wi-Fi network in a new office building. You want everyone to have a strong signal, but the building is full of walls, furniture, and corners that block or bounce the invisible radio waves. If you just guess where to put the routers, you might end up with dead zones where the internet doesn't work at all.

This paper is like a digital "crystal ball" that helps engineers predict exactly how those radio waves will behave before they even buy a single router.

Here is the story of how they built this crystal ball, broken down into simple steps:

1. The Problem: The Invisible Maze

Radio waves are tricky. They don't just travel in a straight line like a laser pointer; they bounce off walls, slide around corners, and get absorbed by furniture. Inside a building, this is a chaotic maze.

The Analogy: Imagine shouting in a room full of mirrors and heavy curtains. Your voice bounces off the mirrors (reflections) but gets muffled by the curtains (absorption). If you want to know if someone in the next room can hear you, you can't just guess; you need to map out exactly how your voice travels.

2. The Solution: A Three-Part Recipe

The authors created a step-by-step framework (a recipe) to simulate this maze using three different software tools, working together like a kitchen team:

Step 1: The Blueprint (SketchUp)
First, they went to the actual building (the 1st floor of an engineering department in India) and measured every wall and room with a tape measure and a laser. They then built a 3D digital twin of the floor in a program called SketchUp.
- Think of this as building a perfect Lego model of the building inside a computer.
Step 2: The Simulation (Wireless Insite)
Next, they fed that Lego model into a powerful, expensive software called Wireless Insite. This software acts like a super-fast weather forecaster, but instead of rain, it predicts radio waves. It shoots thousands of invisible "rays" from a transmitter (the router) and watches how they bounce, bounce, and bounce off the digital walls and furniture.
- Think of this as running a video game where the "characters" are light beams, and the game engine calculates exactly where they land.
Step 3: The Report Card (MATLAB)
Finally, they took the raw data from the simulation and used MATLAB to turn it into a readable chart called a Power Delay Profile (PDP).
- Think of this as the teacher grading the test. It tells you: "The signal arrived here strong, but over there, it's weak because it hit a metal table."

3. The "Aha!" Moment: Finding the Dead Zones

To prove their method worked, they ran a test. They placed a virtual router in the hallway and checked the signal strength in every room.

The Result: The simulation showed that while the hallway had a great signal, the faculty offices were "coverage holes"—dark spots where the signal was too weak to use.
The Lesson: If they had just guessed, they might have put the router in the hallway and assumed everyone was fine. This tool told them, "Nope, you need to move the router or add more ones to fix the holes."

4. The "Magic Trick": Changing Materials

They also did a fun experiment to show how smart the simulation is. They took the same room and told the computer, "Pretend all the wooden desks are now made of shiny metal."

What happened? The simulation instantly changed. The radio waves bounced off the metal desks much more strongly, creating a completely different pattern of signal strength.
Why it matters: This proves the tool is sensitive enough to know that a wooden chair acts differently than a metal one. It's like the software has "eyes" to see what the building is made of.

Why Should You Care?

This paper isn't just about fancy math; it's about saving time and money.

Without this: You buy routers, install them, realize half the office has no internet, buy more routers, move them, and waste weeks of work.
With this: You run the simulation on your computer first. You see the "dead zones" instantly, move the virtual router, and then you buy and install the real equipment in the perfect spot.

In a nutshell: This paper gives engineers a virtual test drive for their Wi-Fi networks, ensuring that when they finally flip the switch, the internet works perfectly everywhere, from the hallway to the deepest office.

1. Problem Statement

Indoor wireless propagation modeling is critical for network planning, as over 80% of wireless data traffic occurs indoors. However, accurately predicting signal behavior in these environments is challenging due to complex multipath effects, including reflections, diffractions, and material interactions. Traditional models often fail in cluttered indoor spaces or high-frequency bands (mmWave/sub-THz) where radio waves behave similarly to light. Furthermore, Global Positioning System (GPS) and satellite imagery are ineffective indoors, making it difficult to measure and predict signal strength and coverage without extensive, time-consuming field measurements. There is a need for a robust, step-by-step framework that integrates 3D modeling, electromagnetic simulation, and channel parameter extraction to predict coverage holes and optimize network deployment.

2. Methodology

The authors propose a comprehensive, four-stage workflow to model indoor wireless propagation using a combination of commercial and open-source tools. The framework was applied to the first floor of the Electronics and Communication Engineering (ECE) department at the National Institute of Technology (NIT) Durgapur, India.

The Four-Stage Workflow:

Field Measurement & Data Collection:
- Physical dimensions of the environment (corridors, rooms, laboratories) were measured using a tape measure and a Bosch GLM-40 laser distance measurer.
- Key parameters included floor height (3.44m), total length (34.8m), and width (14.37m), along with specific room dimensions.
3D Layout Construction (SketchUp®):
- The 2D measurements were converted into a 3D model using SketchUp.
- Tools such as the Line, Rectangle, Push/Pull (Extrude), and Paint Bucket were used to define walls, doors, windows, and furniture.
- Materials and textures were assigned to surfaces to simulate real-world properties.
- The final model was exported as a .kmz file for simulation.
Ray Tracing Simulation (Wireless Insite®):
- The SketchUp model was imported into Wireless Insite, a proprietary electromagnetic propagation software.
- Simulation Setup:
  - Antennas: Isotropic or horn antennas were selected.
  - Configuration: Transmitter (TX) and Receiver (RX) points were strategically placed.
  - Parameters: Sinusoidal signal at 1 GHz center frequency, 10 kHz bandwidth, and a maximum of 25 rays.
- The software calculated propagation paths, path loss, complex impulse response (CIR), and delay spread.
Post-Processing & Analysis (MATLAB®):
- Output data from Wireless Insite (specifically CIR) was processed in MATLAB.
- The authors generated Power Delay Profiles (PDP) and constructed tapped-delay-line channel models for further analysis, such as Bit Error Rate (BER) simulation.

Verification Process:

Geometric Verification: The time of arrival (ToA) of the first Line-of-Sight (LOS) path was compared against the physical distance between TX and RX to ensure simulation scaling accuracy.
Material Verification: Simulations were run with different material properties (wooden vs. metallic furniture) to verify that the software correctly modeled absorption (wood) versus strong reflection (metal).

3. Key Contributions

End-to-End Framework: The paper provides a reproducible, step-by-step guide integrating SketchUp, Wireless Insite, and MATLAB for indoor channel modeling.
Material Sensitivity Analysis: The study explicitly demonstrates how changing material properties (e.g., from wood to metal) significantly alters ray tracing results, highlighting the importance of accurate material assignment in simulation.
Coverage Hole Identification: The framework successfully identified "coverage holes" in specific areas (faculty rooms) when a single transmitter was used, demonstrating its utility for network planning.
Cross-Validation: The authors validated the simulation outputs against physical distance measurements and theoretical propagation speeds, establishing confidence in the model's accuracy.

4. Results

Simulation Accuracy: The verification step confirmed that the simulated path length (derived from ToA $\times$ speed of light) matched the physical distance ( $33.43m$ vs. $33.44m$ ), validating the geometric scaling of the model.
Material Impact: Ray tracing results showed that wooden furniture resulted in lower signal strength due to absorption (dominated by blue rays in visualization), whereas metallic furniture created strong reflections and additional dominant paths (yellow/green rays) alongside the LOS path.
Coverage Analysis: In a use case with a single transmitter placed in the corridor:
- Corridor (RX1): Received an average signal strength approximately 40 dB higher than receivers in laboratories.
- Faculty Rooms: These areas were identified as coverage holes, receiving very weak signals due to blockage by walls and lack of LOS.
- Conclusion: The initial transmitter placement was suboptimal, necessitating further network planning iterations.

5. Significance and Future Work

Significance: This work bridges the gap between theoretical ray tracing and practical indoor network deployment. It offers a cost-effective alternative to exhaustive field measurements by allowing engineers to interpolate and extrapolate signal data from limited points. It is particularly relevant for the planning of 5G and future mmWave networks where indoor coverage is critical.
Future Scope:
- Optimization: The authors plan to use the framework for WiFi Access Point (AP) location optimization, dividing the area into a $1m \times 1m$ grid to find the position with maximum coverage.
- Validation: Future work involves cross-validating these RT models with actual physical measurements and comparing results with other simulation tools (e.g., Sionna, NIST Q-D tool, RANPLAN).
- Modeling: Validating measurement-based models against RT models for the same TX/RX configurations.

In summary, the paper presents a validated, practical framework for simulating indoor wireless channels, demonstrating its ability to predict coverage gaps and the critical impact of environmental materials on signal propagation.