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Digital Communication Algorithms with Python

Project description

Build Status Coverage PyPi Docs

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)

Filters

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

Impairments

  • Carrier Frequency Offset (CFO)

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.
  • Convert channel matrix to Bit-level representation.
  • Computation of LogLikelihood ratio using max-log approximation.

Sequences

  • PN Sequence
  • Zadoff-Chu (ZC) Sequence

Utilities

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

Links

  • Estimate the BER performance of a link model with Monte Carlo simulation.
  • Link model object.
  • Helper function for MIMO Iteration Detection and Decoding scheme.

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

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, 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: veeresht@gmail.com

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

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