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CodeEnv: Learning Bit-Flipping Decoding

This repository includes a collection of Python and Tensorflow scripts to learn Bit Flipping decoding through Reinforcement Learning.

Additional details are explained in this paper:


Parity Check matrices

The Parity Check (PC) matrices for the following codes are contained in the Hmat folder:

  • Reed–Muller (RM) codes:

    • RM(2,5)
    • RM(3,6)
    • RM(3,7)
    • RM(4,7)
  • Bose–Chaudhuri–Hocquenghem (BCH) codes:

    • BCH(63,45)

The suffix denotes:

  • _std: standard PC matrix
  • _oc: overcomplete PC matrix (the rows are dual codewords)

Getting Started

The main code is contained in the files:

  • CodeEnv.py: environment class based on OpenAI Gym
  • utils.py: various helper functions (mainly to implement the permutation preprocessing based on the code's automorphism group)

The actual optimization uses RLlib and Tune, both of which are based on Ray.

Workflow

0) Initialization

Make sure Ray and RLlib are installed. An example of Anaconda environment is provided in rllib.yml.

1) Update config settings and log folder

In opt.py, edit the following:

codedir = "<MY_PATH>"
tmpdir = "<TEMP_RAY_DIR>"

Then, copy the folder Hmat to the project directory, i.e., codedir/Hmat. The log folder is set to codedir/ray_results. The Ray session will store temporary files in tmpdir/ray.

Note that, in opt.py, the training hyperparameters can be changed. Moreover, accordingly to computation capabilities, the number of CPU/GPU may be edited as well:

"num_gpus" : <N_GPUs>,
"num_workers": <N_CPUs>

2) Run the optimization and explore hyperparameters

Use optscript to run opt.py from the terminal. This file just writes the process ID to the file last_pid and calls opt.py. In case something goes wrong, use kill_last_pid to terminate this process and all child processes created by Ray.

Note that a new directory <TRAINED_AGENT_DIR>, containing all the model's files, will be created under codedir/ray_results/CodeEnv/<TRAINED_AGENT_DIR>

Tensorboard can be used with the specified log directory to monitor the training progress.

3) Evaluate the trained model

Finally, use jobscript to evaluate the trained model for a specified range of SNRs. This script calls eval.py which then saves the results into .mat and .txt files.

Modify jobscript, inserting the proper folder for your trained model:

resultPath = "insert-path-to-<TRAINED_AGENT_DIR>"

As in point 1), change in eval.py:

tmpdir = "<TEMP_RAY_DIR>"


Notes

The current scripts replicate the results for RM (2,5) [LBF-NN] and [s+d LBF-NN].

Please send any feedback, comment or request to Fabrizio Carpi or Christian Häger.

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Reinforcement Learning for Bit Flipping decoding of linear codes

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