CommPy

CommPy is an open source toolkit implementing digital communications algorithms in Python using NumPy and SciPy.

Objectives

To provide readable and useable implementations of algorithms used in the research, design and implementation of digital communication systems.

Available Features

Channel Coding

Encoder for Convolutional Codes (Polynomial, Recursive Systematic). Supports all rates and puncture matrices.
Viterbi Decoder for Convolutional Codes (Hard Decision Output).
MAP Decoder for Convolutional Codes (Based on the BCJR algorithm).
Encoder for a rate-1/3 systematic parallel concatenated Turbo Code.
Turbo Decoder for a rate-1/3 systematic parallel concatenated turbo code (Based on the MAP decoder/BCJR algorithm).
Binary Galois Field GF(2^m) with minimal polynomials and cyclotomic cosets.
Create all possible generator polynomials for a (n,k) cyclic code.
Random Interleavers and De-interleavers.
Belief Propagation (BP) Decoder and triangular systematic encoder for LDPC Codes.

Channel Models

SISO Channel with Rayleigh or Rician fading.
MIMO Channel with Rayleigh or Rician fading.
Binary Erasure Channel (BEC)
Binary Symmetric Channel (BSC)
Binary AWGN Channel (BAWGNC)

Wifi 802.11 Simulation Class

A class to simulate the transmissions and receiving parameters of physical layer 802.11 (currently till VHT (ac)).

Filters

Rectangular
Raised Cosine (RC), Root Raised Cosine (RRC)
Gaussian

Modulation/Demodulation

Phase Shift Keying (PSK)
Quadrature Amplitude Modulation (QAM)
OFDM Tx/Rx signal processing
MIMO Maximum Likelihood (ML) Detection.
MIMO K-best Schnorr-Euchner Detection.
MIMO Best-First Detection.
Convert channel matrix to Bit-level representation.
Computation of LogLikelihood ratio using max-log approximation.

Utilities

Decimal to bit-array, bit-array to decimal.
Hamming distance, Euclidean distance.
Upsample
Power of a discrete-time signal

FAQs

Why are you developing this?

During my coursework in communication theory and systems at UCSD, I realized that the best way to actually learn and understand the theory is to try and implement ''the Math'' in practice :). Having used Scipy before, I thought there should be a similar package for Digital Communications in Python. This is a start!

What programming languages do you use?

CommPy uses Python as its base programming language and python packages like NumPy, SciPy and Matplotlib.

How can I contribute?

Implement any feature you want and send me a pull request :). If you want to suggest new features or discuss anything related to CommPy, please get in touch with me (veeresht@gmail.com).

How do I use CommPy?

Requirements/Dependencies

python 3.2 or above
numpy 1.10 or above
scipy 0.15 or above
matplotlib 1.4 or above
nose 1.3 or above
sympy 1.7 or above

Installation

To use the released version on PyPi, use pip to install as follows::

$ pip install scikit-commpy

To work with the development branch, clone from github and install as follows::

$ git clone https://github.com/veeresht/CommPy.git
$ cd CommPy
$ python setup.py install

conda version is curently outdated but v0.3 is still available using::

$ conda install -c https://conda.binstar.org/veeresht scikit-commpy

Citing CommPy

If you use CommPy for a publication, presentation or a demo, a citation would be greatly appreciated. A citation example is presented here and we suggest to had the revision or version number and the date:

V. Taranalli, B. Trotobas, and contributors, "CommPy: Digital Communication with Python". [Online]. Available: github.com/veeresht/CommPy

I would also greatly appreciate your feedback if you have found CommPy useful. Just send me a mail: veeresht@gmail.com

For more details on CommPy, please visit https://veeresht.info/CommPy/

BastienTr/CommPy