A Python simulation framework for designing, testing, and validating wavelength-division multiplexed (WDM) optical communication links — with built-in RC4 encryption, EDFA amplification, and DCF dispersion compensation.
🔗 Live Demo: secure-wdm-simulator.onrender.com
Simulation Controls
Signal Waveforms
Eye Diagrams
BER vs Distance
This simulator lets you model an end-to-end WDM optical link and study how signal integrity degrades over distance. You can configure the fiber length, number of wavelength channels, data rate, and toggle security/amplification layers — then compare results across multiple scenarios through an interactive dashboard.
Key simulation features:
- NRZ signal generation and RC4-based stream encryption
- Optical transmission across configurable fiber spans
- EDFA (Erbium-Doped Fiber Amplifier) noise modeling
- DCF (Dispersion Compensating Fiber) correction
- Bessel-filtered receiver and photodetector modeling
- Q-factor, BER, eye diagrams, and waveform analysis
- Image encryption visualization as a practical security demo
This project demonstrates applied Python across several engineering domains:
Numerical Computing — numpy
All signal arrays, bit sequences, noise injection, and physical channel math (attenuation, dispersion) are built on NumPy vectorised operations, avoiding loops for performance.
Signal Processing — scipy
SciPy's signal module powers the Bessel low-pass filter applied at the receiver. Statistical functions compute Q-factor from the sampled eye distribution.
Data Analysis — pandas
Scenario comparison tables and summary metrics are structured with Pandas DataFrames for clean display and easy export.
Interactive Visualisation — plotly
BER-vs-distance curves on a log scale, eye diagrams with overlaid traces, and multi-channel WDM spectrum plots are rendered with Plotly for interactivity inside the browser.
Static Plotting — matplotlib
Waveform subplots (Input NRZ, Encrypted RC4, Optical TX/RX, Photocurrent, After Filter) are generated with Matplotlib and embedded directly into the Streamlit UI.
Image Processing — Pillow
The image encryption tab uses Pillow to apply RC4-based pixel scrambling and demonstrate visual cipher effects on bitmap data.
Web Application — streamlit
The entire interactive dashboard — sliders, dropdowns, toggles, multi-tab results, and live metric cards — is built purely in Python with Streamlit, with no JavaScript required.
Modular Architecture The codebase is organised into domain-specific Python packages, each with a clear responsibility:
WDM-simulation/
├── signal_generation/ # NRZ bit stream generation
├── modulation/ # Optical modulator (NRZ → optical power)
├── encryption/ # RC4 stream cipher implementation
├── fiber_channel/ # Attenuation, dispersion, noise (ASE)
├── receiver/ # Photodetector, Bessel filter, BER/Q calculation
├── analysis/ # Eye diagram construction, scenario comparison
├── simulation/ # Pipeline orchestrator tying all modules together
├── ui/ # Streamlit page components and layout
├── config.py # Centralised simulation constants
└── main.py # Entry point
git clone https://github.com/parinith-web/WDM-simulation.git
cd WDM-simulation
pip install -r requirements.txt
streamlit run main.pyThen open http://localhost:8501 in your browser.
| Library | Version | Role |
|---|---|---|
numpy |
≥ 1.24 | Numerical arrays and signal math |
scipy |
≥ 1.10 | Bessel filter and statistics |
matplotlib |
≥ 3.7 | Waveform subplot rendering |
streamlit |
≥ 1.55 | Interactive web dashboard |
plotly |
≥ 5.17 | BER curves and eye diagrams |
pandas |
≥ 2.0 | Scenario tables and metrics |
Pillow |
≥ 10.0 | Image encryption visualisation |