CommPy

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Digital Communication with Python

CommPy 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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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](https://github.com/veeresht/CommPy/tree/master/commpy/channelcoding)
--------------
- 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](https://github.com/veeresht/CommPy/blob/master/commpy/channels.py)
--------------
- 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](https://github.com/veeresht/CommPy/blob/master/commpy/wifi80211.py)
- A class to simulate the transmissions and receiving parameters of physical layer 802.11 (currently till VHT (ac)).

[Filters](https://github.com/veeresht/CommPy/blob/master/commpy/filters.py)
-------
- Rectangular
- Raised Cosine (RC), Root Raised Cosine (RRC)
- Gaussian

[Impairments](https://github.com/veeresht/CommPy/blob/master/commpy/impairments.py)
-----------
- Carrier Frequency Offset (CFO)

[Modulation/Demodulation](https://github.com/veeresht/CommPy/blob/master/commpy/modulation.py)
-----------------------
- 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.

[Sequences](https://github.com/veeresht/CommPy/blob/master/commpy/sequences.py)
---------
- PN Sequence
- Zadoff-Chu (ZC) Sequence

[Utilities](https://github.com/veeresht/CommPy/blob/master/commpy/utilities.py)
---------
- Decimal to bit-array, bit-array to decimal.
- Hamming distance, Euclidean distance.
- Upsample
- Power of a discrete-time signal

[Links](https://github.com/veeresht/CommPy/blob/master/commpy/links.py)
-----
- 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
- 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/

CommPy

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