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 and triangular systematic encoder for LDPC Codes.
- 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)
- A class to simulate the transmissions and receiving parameters of physical layer 802.11 (currently till VHT (ac)).
- 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
- 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.
- PN Sequence
- Zadoff-Chu (ZC) Sequence
- Decimal to bit-array, bit-array to decimal.
- Hamming distance, Euclidean distance.
- Upsample
- Power of a discrete-time signal
- Estimate the BER performance of a link model with Monte Carlo simulation.
- Link model object.
- Helper function for MIMO Iteration Detection and Decoding scheme.
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 (veeresht@gmail.com).
- 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
- 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
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/