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OSD-CNN: CNN-Enhanced LDPC NMS/OSD Decoder

中文版本 / Chinese Version | English Version

This repository contains a complete end-to-end experiment of NMS→OSD→CNN hybrid decoding in a single Jupyter Notebook (osd_cnn.ipynb): from CCSDS standard LDPC code loading, mixed SNR data generation, DIANet training, comparison simulation of NMS/OSD-1/OSD-2/CNN-OSD, and visualization. The current implementation uses a unified NMS normalization factor alpha=0.78, consistent across training and simulation phases to ensure data distribution alignment.

✨ Features

  • 📦 Standard LDPC Code Support: Loads CCSDS (128, 64) code from Alist format file, automatically generates G matrix via Gaussian elimination.
  • 🔁 Unified NMS Normalization Strategy: Both decode_nms_trajectory and hybrid OSD use the same alpha=0.78, facilitating experiment reproduction and avoiding data distribution drift.
  • 📚 Mixed-SNR Training Data: Training set covers 2.0–4.0 dB, with multi-SNR samples sharing the same NMS configuration.
  • 🧠 DIANet Trajectory Learning: Takes NMS LLR trajectory (n, T) as input, outputs soft information gain for each bit.
  • 🎯 OSD Algorithm Implementation: Supports OSD-1 and OSD-2, includes adaptive Gaussian elimination and Top-K acceleration optimization.
  • 🛟 CNN-OSD Fusion: hybrid_soft = y + 0.78 * cnn_logits, directly uses enhanced soft information in OSD-1/2.
  • 📈 End-to-End Visualization: Notebook includes FER curves, gain bar charts, and performance comparison tables, showing results for both OSD-1 and OSD-2.

📁 Repository Layout

osd-cnn/
├── osd_cnn.ipynb              # Main experiment Notebook (code + documentation)
├── dia_model_best.pth         # Trained DIANet model parameters
├── CCSDS_ldpc_n128_k64.alist  # CCSDS standard LDPC code in Alist format
├── README.md                   # This document (English)
└── README_CN.md                # Chinese version

⚡ Quick Start

Option 1: Google Colab

  1. Open Colab directly.
  2. Runtime → Change runtime type → Select GPU.
  3. Run cells sequentially. The notebook will automatically install dependencies, generate data, train DIANet, and run simulations.

Option 2: Local Environment

  1. Install dependencies (if not auto-installed by the notebook, refer to requirements and install via pip).
  2. Start Jupyter: jupyter notebook.
  3. Open osd_cnn.ipynb and execute all cells in order.

🧩 Core Components

Module Content Highlights
LDPC Code Loading Loads CCSDS (128, 64) code from Alist file, generates G matrix via Gaussian elimination Supports standard format, auto-verifies H@G.T=0
NMS Decoding + Trajectory Recording decode_nms_trajectory, fixed alpha=0.78 Records iterative LLR trajectory, outputs failure samples
Mixed-SNR Data Generation Only keeps NMS failure frames, TOTAL_SAMPLES = 60000 SNR randomly sampled but shares same alpha
DIANet Training BCEWithLogitsLoss + Adam (1e-3) + EarlyStopping Takes trajectory as input, predicts bit error tendency
OSD Algorithm OSD-1/OSD-2, adaptive Gaussian elimination, Top-K acceleration Supports standard OSD and CNN-enhanced hybrid OSD
CNN-OSD Fusion hybrid_soft = y + 0.78 * cnn_logits Compares FER/BER/time with pure OSD
Visualization FER curves, gain bar charts, performance tables Shows overall performance of NMS / OSD-1/2 / CNN-OSD

🧮 Key Hyper-Parameters

  • MAX_ITER_NMS = 12: NMS iteration count.
  • alpha = 0.78: NMS normalization factor, uniformly used as fixed value in both training and simulation phases to ensure consistent data distribution.
  • MAX_FRAMES = 1,000,000, MIN_CNN_ERRORS = 50: Simulation termination conditions.
  • BATCH_SIZE = 512: DIANet training batch size.
  • TOTAL_SAMPLES = 60,000: Mixed-SNR failure frames for training.
  • R = 0.5: Code rate (CCSDS (128, 64) code).

📊 Latest Simulation Snapshot

Simulation uses CCSDS (128, 64) code, unified alpha=0.78, SNR range 2.0-4.0 dB (step 0.2 dB).

📚 References

  1. Gallager, R. G. Low-density parity-check codes, 1962.
  2. Fossorier, M. P. C., & Lin, S. Soft-decision decoding of linear block codes, 1995.
  3. Chen, J., & Fossorier, M. P. C. Near optimum universal belief propagation based decoding of LDPC codes, 2002.
  4. Jia, M. et al. Ordered Statistics Decoding for Short Block Codes, 2019.