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

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

• 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 for LDPC Codes.

• Binary Erasure Channel (BEC)
• Binary Symmetric Channel (BSC)
• Binary AWGN Channel (BAWGNC)

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

• Carrier Frequency Offset (CFO)

• Phase Shift Keying (PSK)
• Quadrature Amplitude Modulation (QAM)
• OFDM Tx/Rx signal processing

• PN Sequence
• Zadoff-Chu (ZC) Sequence

• Decimal to bit-array, bit-array to decimal.
• Hamming distance, Euclidean distance.
• Upsample

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!

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

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 ([email protected]).

• python 2.7 or above
• numpy 1.10 or above
• scipy 0.15 or above
• matplotlib 1.4 or above
• nose 1.3 or above

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

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

If you use CommPy for a publication, presentation or a demo, I request you to please cite CommPy as follows:

Veeresh Taranalli, "CommPy: Digital Communication with Python, version 0.3.0. Available at https://github.com/veeresht/CommPy", 2015.

I would also greatly appreciate your feedback if you have found CommPy useful. Just send me a mail: [email protected]

For more details on CommPy, please visit http://veeresht.github.com/CommPy