A Python-based toolkit to simulate, visualize, and analyze digital modulation schemes. Ideal for signal processing learners, engineers, and researchers.

Supports QPSK, 16-QAM, AWGN channel modeling, BER analysis, and constellation visualization. More schemes like 32-QAM, 128-QAM, and OFDM coming soon!

• ✅ QPSK Modulation/Demodulation
• ✅ 16-QAM with Constellation Mapping
• ✅ AWGN Simulation with configurable SNR
• ✅ BER Calculation across SNR range
• ✅ Constellation Plots (ideal, noisy, rotated)
• 🚧 OFDM (Coming soon)
• 🚧 32-QAM, 64-QAM, 128-QAM Support (Planned)

🎯 Roadmap

• QPSK with BER and noise visualization
• QAM basic implementation
  • 4QAM, 16QAM, 32QAM, 64-QAM, 128-QAM
  • OFDM simulation with FFT/IFFT
  • Channel modeling (multipath, fading, Doppler)
  • CLI tool or Web dashboard

📚 Digital Communications / Coding Theory

Subtopics:

• Forward Error Correction (FEC):
• convolutional codes → Viterbi algorithm, trellis decoding
• block codes → Reed-Solomon, Hamming codes, LDPC, BCH
• Decoding Algorithms:
  • Viterbi (for convolutional codes)
  • Berlekamp-Massey (for Reed-Solomon)
  • Modulation and Coding Schemes (MCS):
    • Combined use of modulation (QAM, PSK) with coding
    • Channel Capacity & Noise Modeling:
    • AWGN, Rayleigh fading, etc.

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Modulation techniques like QAM (Quadrature Amplitude Modulation) are critical in satellite communication systems because they directly impact data rate, bandwidth efficiency, and robustness against noise and interference. Here’s a concise breakdown:

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✅ Modulation Matters in Satellite Modems

1. Efficient Use of Bandwidth • Satellites operate over limited spectrum (e.g., Ku, Ka bands), so using higher-order modulations like 16-QAM, 64-QAM allows transmitting more bits per symbol. • This increases data throughput without needing more bandwidth.

2. Trade-off Between Data Rate and Robustness • QPSK (4-QAM) is more robust to noise and phase distortions (important for long-distance satellite links with weak signals). • 16-QAM or higher increases data rates but is more susceptible to errors, so it’s typically used under good SNR conditions.

3. Adaptive Modulation • Many satellite modems use adaptive modulation, adjusting between QPSK, 8-PSK, 16-QAM, etc., depending on link quality (e.g., rain fade in Ka-band). • This ensures maximum efficiency and link reliability.

4. Demodulation at Receiver • Satellite receivers need to demodulate signals accurately even with impairments like: • Doppler shift • Phase noise from LNB/oscillators • Non-linearities from power amplifiers

Hence, modulation schemes are designed to be resilient and recoverable under these conditions.

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🛰️ Summary:

QAM and related modulation techniques are the foundation of reliable and efficient satellite communications, enabling high-speed data transfer while adapting to harsh and variable space link conditions.

Let me know if you want a comparison chart between QPSK, 8PSK, and 16-QAM for satellite use.

🤝 Contributing

Pull requests are welcome! Whether you’re fixing a bug, adding a new scheme, or improving visualizations — all help is appreciated. 1. Fork the repo 2. Create your feature branch: git checkout -b feature/qam32 3. Commit your changes 4. Push and open a PR

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📘 License

This project is licensed under the MIT License. See LICENSE for details.

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📡 About the Author

👋 Created by Vincent Bevia — a senior software engineer and DSP enthusiast.

Worked as a Telecommunication Engineer at EFData and Fairchild with hands-on experience in satellite system engineering, signal processing, and communications link analysis.

Feel free to reach out for feedback, suggestions, or collaboration!