rodsveiga / decoders

Python/NumPy implementations of commonly used decoders for LDPC codes.

Message passing based decoders for LDPC codes with Python/NumPy. Includes implementations of

• min-sum (MSA) and sum-product (SPA) algorithms using sparse matrices (scipy.sparse)
• maximum-likedlood (ML) and linear-programming (LP) decoders (only for short length codes like Hamming(7,4)) based on Using Linear Programming to Decode Binary Linear Codes
• ADMM decoder based on Decomposition Methods for Large Scale LP Decoding

for binary erasure (BEC), binary symmetric (BSC) and binary-AWGN (biawgn) channels.

Dependencies

Following Python/package versions (or higher) are required.

• Python version 3.5.2
• numpy version 1.12.0
• scipy version 0.18.1

Exploring pre-computed results

Checkout assests branch to see all pre-compupted results. These include different codes, simulation results and plots.

Starting from scratch

Running simulations

• Make directories codes and data in the root directory.

• Execute python src/codes.py 10 1200 3 6 to generate 10 random samples from LDPC(1200,3,6) ensemble.

• Run simulations using run_sims.sh {PARA} {CASE} {ARGS} command. For example,

  • ./run_sims.sh SEQL HMG executes all Hamming code related simulations sequentially.
  • ./run_sims.sh SEQL REG_ENS --data-dir=./data --console executes some regular LDPC related simulations sequentially while printing logs onto console.
  • ./run_sims.sh PARA REG_ENS --data-dir=./data executes the same in parallel.
  • Use the latter only on a dedicated server as it will take large amount of CPU.
  • See run_sims.sh for other choices of {CASE}.

• If running the simulations on Niagara cluster, need to setup environment first by executing setup_env.sh.

• Execute ./run_sims.sh PARA HMG --data-dir=$SCRATCH to test if simulations run properly.

• All simulations can be submitted for later excution on Niagara by using command sbatch niagara/submit_job.sh.

Generating plots

• Make directory plots in the root directory.
• Execute ./plot_results.py HMG to view and save Hamming code related plots in PNG format.
• Execute ./plot_results.py REG_ENS --ext=pdf to view and save regular ensemble related plots in PDF format.
• Execute ./plot_results.py HMG REG_ENS --ext=png --data-dir=./data --plots-dir=./plots --silent --error=ber to silently save both Hamming code related and regular ensemble related bit-error-rate plots.
• If running on the Niagara cluster execute ./plot_results.py HMG REG_ENS --data-dir=$SCRATCH --plots-dir=$SCRATCH --silent --agg to use the proper back-end for matplotlib.

Specific commands

Simulations

• python src/main.py bec 1200_3_6_rand_ldpc_1 SPA --codeword=1 --console --params .5 .475 .45 .425 .4 .375 .35 .325 .3
• python src/main.py biawgn 7_4_hamming SPA --codeword=1 --params .01 .05 .1 .5 1 2 4 6 --console
• See run_sims.sh and simulations.py for more.

Ensemble average

• python src/stats.py bec 1200_3_6_rand_ldpc SPA

Plots

• python src/graph.py bec 1200_3_6_ldpc SPA single --error ber
• python src/graph.py bsc 7_4_hamming SPA ML comp_dec --error wer
• See plot_results.py for more.

Generating optimal irregular LDPC codes

First of the following plots the density evolution while the second generates the optimal irregular distribution a given check node distribution (rho).

• python src/ldpc.py plt
• python src/ldpc.py irg --count=10 --len=1200 --rho=5 --rate=.5

Luby-Transform (LT) code

Execute the following to reproduce the Figure 50.4 (histogram for LT code with length 10000) in Information Theory, Inference, and Learning Algorithms by David J.C. MacKay. Replace in the first command with 0.01, 0.03, 0.1.

• python -u src/luby.py 10000 12000 <c> .5 250 --pool=4
• python src/luby_graph.py .01 .03 .1